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Big data analytics uncovers hidden patterns through classification, prediction and reinforcement of big datasets. In these datasets, some features have a negligible connection with other features and some may be insignificant as their presence does not impact the results of big data analytics. The algorithms of big data analytics generate better classification models when supplied with a dataset consisting of relevant, important and informative features. These features can be classified as important and unimportant. For the selection of important features, different filtrations techniques are used. These techniques filter features on different basis like information gain, information dispersion, Gini index, etc. and have a few drawbacks reviewed in this paper. The first contribution of this paper is to propose a new feature selection technique named "Relevance-diversity algorithm" for selecting important features based on two measures i.e. relevance and diversity for optimizing features as low as possible and reducing the search time used in feature selection. The second contribution of the paper is that it proposes a new supervised classification algorithm based on Naive Bayes classification. The assumption of naive i.e. feature independence is discarded from the algorithm of Naive Bayes classification. The features are considered to be dependent on each other and their combined impact on the class value is evaluated. The newly proposed classification algorithm is then applied to the features selected through the relevance-diversity based feature selection technique. The datasets of Weather, Tic-Tac-Toe, Lenses, Balance-scale and CarEval are used for the evaluation of both the techniques. The results of the proposed feature selection method are compared with the existing methods and the results of Modified-Bayes are compared with the existing Naive Bayes algorithm. Analysis revealed that the proposed method performed better in terms of the number of features, accuracy and time complexity. [ABSTRACT FROM AUTHOR]

Classification methods relying on multidimensional features have been widely applied in commerce, healthcare, and transportation. Nevertheless, haphazardly selecting inappropriate features may not only hinder the improvement of classification performance but also disrupt the convergence of classifier parameters, leading to prolonged classifier runtime and an increased storage burden. Therefore, this paper proposed a novel framework (dubbed as DFS-WR) to perform dual selection and weighted representation of multidimensional features. Firstly, DFS-WR employs Laplacian scores to conduct a de-redundant operation as the first feature selection. Subsequently, DFS-WR utilizes a correlation coefficient matrix and redundant sorting to eliminate duplicate deployment of similar features in classification through the second selection. Finally, for hierarchical expression, DFS-WR assigns weights by virtue of information gain (IG), thereby emphasizing the significance of dominant features. The performance of DFS-WR was further evaluated across four aspects: classification ability, runtime performance, storage occupancy, and portability and universality, using five public datasets and eleven widely adopted classifiers. Additionally, this paper considered the proper configuration of crucial parameters within DFS-WR and compared it with other related works. The results indicated that the dual selection strategy for eliminating redundancy and similarities among features could improve the performance of classifiers, and the weighted process based on IG outperformed unweighted and ReliefF-based weighted methods. By employing DFS-WR to multidimensional features, the classifier's runtime and data storage capacity were effectively reduced. In addition, it was proved that the deployment of DFS-WR generally had no limitations on datasets and classifiers, underscoring its good portability and universality in practical applications. • A dual selection and weighting framework (DFS-WR) for classification is introduced. • A method for selecting similar features combined with redundant sorting is proposed. • Using DFS-WR can improve classifier performance, reduce runtime, and decrease storage. • DFS-WR has portability and universality for datasets and classifiers in applications. [ABSTRACT FROM AUTHOR]

• Pinch force strength is accurately predicted using sEMG. • Different pinch movements are accurately classified using sEMG. • Similarity of EMG features is evaluated. • An EMG feature-to-image method is proposed. The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R 2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand. [ABSTRACT FROM AUTHOR]

In this paper, a novel Improved Invasive Weed Optimization-based Hierarchical Attention Network (IIWO-HAN) is developed to achieve text classification. As for huge datasets, automatic labelling is required to get useful insights. Thus, the text classification becomes very important. IIWO has the quality of randomness and imitating compatibility of weeds colony and HAN makes use of levelled document structure. Hence, these two are integrated together to achieve text classification. Then, the proposed method has been analysed based on five popular parameters namely, Accuracy, Precision, TPR, TNR and FNR. For this purpose, three datasets: Reuters dataset, 20-Newsgroup dataset and Self-created dataset have been utilized where Reuters dataset, 20- Newsgroup dataset are standard datasets and the Self-created dataset consists of 5000 documents comprising of abstracts taken from various reputed journals. Further, the proposed methodology has been compared with an existing Improved Sine Cosine Algorithm (ISCA). It is found that IIWO-HAN results in 81.3%, 86.5%, 84.1%, 89.4% and 12.4% for TPR, TNR, Accuracy, Precision and FNR respectively and achieves better performance as compared to existing ISCA which gives 78.1%, 79.4%, 79.4%, 75.6%, 17.8% as TPR, TNR, Accuracy, Precision and FNR respectively for Reuters dataset. For 20- Newsgroup dataset, IIWO- HAN provides the TPR, TNR, Accuracy, Precision and FNR of 87.5%, 88.4%, 87.1%, 91.4% and 12.4% respectively and 84.1%, 86.5%, 89.4%, 92.4% and 15.8% as TPR, TNR, Accuracy, Precision and FNR respectively for Self-created dataset. It is found that the Precision of 92.47% and Accuracy of 89.4% is achieved using self-created dataset at training data value of 90% which is clearly better than Precision of 77.6% and Accuracy of 82.3% for existing ISCA. [ABSTRACT FROM AUTHOR]

Breast cancer was the most diagnosed form of cancer in 2020. Early diagnosis of breast cancer results in a significant improvement in long-term survival rates. Current methods require consultation with experts, which is expensive and time-consuming and thus may not be accessible to all. This paper seeks to train and evaluate supervised machine learning models for the accurate and efficient detection of breast cancer. The Wisconsin Breast Cancer Database dataset describes 30 attributes of cell nuclei, including, but not limited to, their radius, texture, and concavity. It contains 569 instances, 212 of which are malignant tumors. The Random Forest algorithm outperforms other algorithms in classifying breast tumors as either malignant or benign and is thus selected as our primary model. It is trained on two different subsets of the dataset having 16 and 8 features, respectively, identified with the help of multiple feature selection methods. The Random Forest models are tested post hyperparameter tuning on a holdout set, and accuracies of 100% and 99.30% respectively. The models are also compared with four other machine learning classification algorithms: Support Vector Machine (SVM), Decision Tree, Multilayer Perceptron, and K-Nearest Neighbors. The results confirm that Random Forest is the superior method for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

A spam message is forced data in a public, private or commercial network that occupies resources and affects the reliability of the network. This paper investigates a two-level filter-based hybrid model to identify spam content or messages accurately. At level-1 of this model, a high-level filter is incorporated for removing the non-relevant and non-significant features and contents. At level-2, a fuzzy-based composite evaluator is integrated for low-level filtration and to identify the most contributing and effective features. In this composite filter, ChiSquare and ReliefF rankers are computed on each significant feature. Two-phase fuzzy is applied to these ranking methods for generating a reduced and relevant featureset. In the final stage, the Naive Bayes and random forest classifiers are combined using the majority voting method to generate a probabilistic score and detect the spam messages. The proposed model is implemented on CSDMC2010 SPAM, spambase, and SMS Spam Collection datasets. The analytical evaluation is conducted on the error and accuracy-based performance measures. The comparative analysis is done against various conventional filters, classifiers, and stage-of-art methods. The comparative results identified that the proposed model achieved an average accuracy of 98.80% on CSDMC2010, 97.79% on spambase, and 98.84% on SMS Spam collection datasets and outperformed the existing conventional and recent algorithms and models. [ABSTRACT FROM AUTHOR]

Over the past decades, rice crops are crucially admitted as one of the powerful energy streams for the production of resources. Rice plant diseases are considered as a raising factor behind the agricultural, economic and communal loss in the upcoming development of the agricultural field. Since last 10 years diagnosis of plant disease in approach to image processing techniques have remained keen are of interest among the researcher. A number of disease detection, identification and quantification methods have been developed and applied in a wide variety of crops. This paper reviews related research papers from the period between 2007 and 2018 with a focus on the development of state of the art. The related studies are compared based image segmentation, feature extraction, feature selection and classification. This paper also outlines the current achievements, limitations, and suggestions for future research associated with the diagnosis of rice plant diseases. [ABSTRACT FROM AUTHOR]

The easy accessibility and simplicity of Short Message Services (SMS) have made it attractive to malicious users thereby incurring unnecessary costing on the mobile users and the Network providers' resources. The aim of this paper is to identify and review existing state of the art methodology for SMS spam based on some certain metrics: AI methods and techniques, approaches and deployed environment and the overall acceptability of existing SMS applications. This study explored eleven databases which include IEEE, Science Direct, Springer, Wiley, ACM, DBLP, Emerald, SU, Sage, Google Scholar, and Taylor and Francis, a total number of 1198 publications were found. Several screening criteria were conducted for relevant papers such as duplicate removal, removal based on irrelevancy, abstract eligibility based on the removal of papers with ambiguity (undefined methodology). Finally, 83 papers were identified for depth analysis and relevance. A quantitative evaluation was conducted on the selected studies using seven search strategies (SS): source, methods/ techniques, AI approach, architecture, status, datasets and SMS spam mobile applications. A Quantitative Analysis (QA) was conducted on the selected studies and the result based on existing methodology for classification shows that machine learning gave the highest result with 49% with algorithms such as Bayesian and support vector machines showing highest usage. Unlike statistical analysis with 39% and evolutionary algorithms gave 12%. However, the QA for feature selection methods shows that more studies utilized document frequency, term frequency and n-grams techniques for effective features selection process. Result based on existing approaches for content-based, non-content and hybrid approaches is 83%, 5%, and 12% respectively. The QA based on architecture shows that 25% of existing solutions are deployed on the client side, 19% on server-side, 6% collaborative and 50% unspecified. This survey was able to identify the status of existing SMS spam research as 35% of existing study was based on proposed new methods using existing algorithms and 29% based on only evaluation of existing algorithms, 20% was based on proposed methods only. This study concludes with very interesting findings which shows that the majority of existing SMS spam filtering solutions are still between the "Proposed" status or "Proposed and Evaluated" status. In addition, the taxonomy of existing state of the art methodologies is developed and it is concluded that 8.23% of Android users actually utilize this existing SMS anti-spam applications. Our study also concludes that there is a need for researchers to exploit all security methods and algorithm to secure SMS thus enhancing further classification in other short message platforms. A new English SMS spam dataset is also generated for future research efforts in Text mining, Tele-marketing for reducing global spam activities. • A comprehensive review of Soft Techniques in SMS Spam Classification (upto May'19). • Overview of the most exploited methods used in feature selection & classification. • Taxonomy of existing state of the art methodologies is developed. • User's acceptability of SMS spam applications on Android App store was evaluated. [ABSTRACT FROM AUTHOR]

The world has recently been exposed to a fierce attack from many viral diseases, such as Covid-19, that exhausted medical systems around the world. Such attack had a negative impact not only on the health status of people or the high death rate, but also had a bad impact on the economic situation, which affected all countries of the world especially the poor and the developing ones. Monkeypox is one of the latest viral diseases that may cause a pandemic in the near future if not dealt and diagnosed with appropriately. This paper provides a new strategy for diagnosing monkeypox, which is called; Accurate Monkeypox Diagnosing Strategy (AMDS). The proposed AMDS consists of two phases, which are; (i) pre-processing and (ii) classification. During the pre-processing phase, the most effective feature are selected using Binary Tiki-Taka Algorithm (BTTA). On the other hand, in the classification phase, ensemble classification is used for diagnosing new cases, which combines evidence from three different new classifiers, namely; (a) Layered K-Nearest Neighbors (LKNN), (b) Statistical Naïve Bayes (SNB), and (c) Deep Learning Classifier (DLC). Moreover, the decisions of the proposed classifiers are merged in a new voting scheme called Fuzzified Voting Scheme (FVS). AMDS has been compared against recent diagnostic strategies. Experimental results have proven that AMDS outperforms other monkeypox diagnostic strategies as it introduces the most accurate diagnosis according to two different datasets. • This paper provides Accurate Monkeypox Diagnosing Strategy (AMDS). • AMDS consists of pre-processing and classification phases. • In pre-processing phase, binary Tiki-Taka algorithm as feature selection is used. • In the classification phase, ensemble classification is used to diagnose new cases. • Results have proven that AMDS outperforms other monkeypox diagnostic strategies. [ABSTRACT FROM AUTHOR]

Text classification is the process of automatically categorizing text documents into predefined labels, gaining increasing importance with the growing volume of data. In the vector space model, terms within documents must be quantified to be used with classifiers. The abundance of unique terms associated with documents can lead to unwieldy vectors. To address this issue, feature selection, reducing the number of terms by removing irrelevant terms, is a common solution. In this paper, a novel feature selection method called Amount of ReDistribution to Establish Neutrality, ARDEN, is proposed for the task of text classification. ARDEN, is designed with statistical distance perspective by measuring the distance of a term to its neutral counterpart, which represents the least distinguishing term having uniform document frequencies across all classes. ARDEN introduces a method to measure the related distance, providing insights into the degree of deviation of the term from neutrality, thus capturing its distinguishing power. ARDEN is experimentally tested against state-of-the-art feature selection methods. The results suggest that it is a competitive feature selection method, exhibiting superior performance in terms of summary statistics, μ and δ ′. Furthermore, ARDEN is designed for multi-class text classification problems, which does not require a globalization function. Additionally, the proposed method is compared against Wasserstein statistical distance metric, and clearly outperforms it. Finally, we also propose δ ′ evaluation criterion in order to facilitate the interpretation of experimental outcomes across multiple feature sizes. • A novel feature selection, ARDEN, is proposed for text classification. • ARDEN measures the amount of redistribution to establish neutrality for ranking. • ARDEN is designed for multi-class problems and does not need globalization functions. • Experimental results suggest that ARDEN is a competitive feature selection method. • δ ′ is proposed to interpret the results of multiple features. [ABSTRACT FROM AUTHOR]

Generating pseudo unseen samples is currently an effective approach for addressing the generalized zero-shot classification (GZSC) problem. However, in practical scenarios, the test set may contain open-set samples without semantic representations. Generalized zero-shot open-set classification (GZSOSC) extends GZSC tasks by simultaneously dealing with samples from seen, unseen, and open-set classes. In this paper, we propose a novel method to learn selective-generative feature representations (SGFR) to tackle the GZSOSC problem. Firstly, to handle the lack of unseen samples, we introduce a simple yet effective unseen feature generation method that leverages the seen–unseen relationships. Through an efficient alternating optimization strategy, we learn the seen–unseen relationships and the unseen visual centers. Secondly, to address the lack of open-set samples, we focus on learning a tightly clustered space for both seen and unseen classes. This enables effective open-set feature selection. We utilize the selected open-set samples to generate high-quality open-set features, thus enhancing the diversity of open-set samples. Extensive experiments are conducted to demonstrate the effectiveness of SGFR in handling the GZSOSC task. [ABSTRACT FROM AUTHOR]

The 3D point cloud, a common format of 3D data, finds extensive applications in fields like remote sensing, surveying, robotics, and more. Addressing the challenges posed by insufficient weighted feature information and the offset between weighted results and task expectations in the attention mechanism, this paper proposes a Multi-scale Learnable Key-channel Attention Network (MLKNet). First, we introduce a feature feedback-repair module to mitigate the impact of information loss in the feature embedding process. This module aims to fully embed the original data into a high-dimensional feature space, ensuring a rich supply of feature information for subsequent transformer modules. Second, an efficient hierarchical local feature encoder extract and aggregate local features from point clouds at various scales, thereby significantly enhancing the model's capability to represent geometric structures. Third, a novel learnable key-channel attention module allows tasks to influence the feature selection and weighting process directly, make the highlighted features as close to task expectations as possible, effectively enhancing the network's perception of global semantic information. Our method was benchmarked on various tasks where we achieved overall accuracy (OA) of 92.3% on the ModelNet40 classification task and achieved instance mean intersection over union (ins. mIoU) of 87.6% on the ShapeNet-part segmentation task. The results indicate the superior performance of our method. • A feature feedback-repair module is used to reduce the loss during data embedding. • A hierarchical local feature encoder is used to aggregate the local features. • A novel matrix definition method is designed in learnable key-channel attention. [ABSTRACT FROM AUTHOR]

Signal selection is a crucial issue for classifiers. A feature neighborhood based method is proposed to select the optimal signals in this paper. The neighborhood of a feature is first defined to classify each example according to whether its feature value belongs to the neighborhood. A discernibility matrix is established based on all the classification results of the feature. The classification ability of a feature is then calculated according to its discernibility matrix. Furthermore, the classification ability of a signal combination is achieved by calculating the disjunction of the corresponding feature discernibility matrices. The optimal signal combination (O-combination) with the highest classification ability is finally selected. The proposed method is validated by using the data from two public databases of human motion. The results show that 2 ∼ 5 signals are selected from over 50 signals to be the O-combinations for human gait mode recognition. The accuracy rates of gait mode recognition (RARs) of these O-combinations are higher than those of other signal combinations with the same dimensionality. The RARs of the 4- and 5- dimensional O-combinations are even higher than that of the combination formed with all signals. Specifically, the RARs of the 4-dimensional O-combinations are 99.37% and 99.86% on the two public databases. In addition, it is found that the signal of clearance between swing foot and stance foot exists in most of the combinations with high RAR, which indicates that this signal cannot be ignored for gait mode recognition. • A signal selection method using for high accuracy gait mode recognition. • Signal classification ability is expressed by the feature neighborhood. • Discernibility matrix is established to calculate classification ability. • 4- and 5- dimensional signal combinations have pretty high recognition accuracy. • Clearance between swing and stance foot is useful for gait mode recognition. [ABSTRACT FROM AUTHOR]

• A machine learning classifier trained with multiple RTUs predicts faults well. • Converting insignificant faulty cases to unfaulty cases reduced false alarms. • A reduced set of practical features showed good performance. • The classifier's prediction accuracy improved with increasing fault severity. • Performance was poor when applied in the field to a system not used in training. There have been many recent developments to take advantage of machine learning for the task of automated fault detection and diagnosis in air-conditioning equipment. A common approach is to develop a machine learning model to classify fault status, using simulated or measurement data, and test the model's fault diagnostic capability on the same system that was used for training. However, such models don't generalize well to different systems. Compounding this challenge, machine learning requires a rich dataset to train the models, and many of the measurement features require additional sensors, all making the potential for practical machine learning AFDD questionable. The current paper focuses on these challenges by: (i) training a model using simulation data from three rooftop units (RTU); (ii) rebalancing the training dataset to reduce false alarms; (iii) developing a feature set that minimizes sensor cost without significantly impacting performance; (iv) testing the model's performance on a set of laboratory measurement data from the same RTUs that were simulated for training data; (v) testing the model on a faulted RTU in the field. The results demonstrate good progress toward practical application of machine learning based AFDD for RTU, but generalization to different systems remains a challenge. [ABSTRACT FROM AUTHOR]

Abstract Support Vector Machine (SVM) has been widely developed for tackling classification problems. Imbalanced data exist in many practical classification problems where the minority class is usually the one of interest. Undersampling is a popular solution for such problems. However, it has the risk of losing useful information in the original data. At the same time, tuning the hyperparameters in SVM is also challenging. By analyzing the geometrical meaning of kernel methods, an approach is proposed in this paper that combines a modified Feature Vector Selection (FVS) method with maximal between-class separability and an easy-tuning version of SVM, i.e. Feature Vector Regression (FVR) proposed in our previous work. In this paper, the modified FVS method selects a small number of data points that can represent linearly all the dataset in the Reproducing Kernel Hilbert Space (RKHS) and the selected data points give also a maximal separability of the imbalanced data in RKHS. The FVR model is also solved analytically, as in least-squared SVM. The decision threshold for classification is optimized to maximize the predefined accuracy metric. Twenty-six imbalanced datasets are considered and comparisons are carried out with several SVM-based methods for imbalanced data. Statistical test shows the effectiveness of the proposed method. Highlights • In this paper, classification of imbalanced data is considered. • A feature vector selection method with respect to maximal separability is proposed. • The decision threshold is optimized considering a specific accuracy metric. • Experiments on 26 public datasets are considered. • Comparisons with various kernel methods show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Missing values are an unavoidable issue in many real-world datasets. One of the most popular approaches to classification with incomplete data is to use imputation to replace missing values with plausible values. However, powerful imputation methods are too computationally intensive when applying a classifier to a new unknown instance. This paper proposes new approaches to integrating imputation, clustering and feature selection for classification with incomplete data in order to improve efficiency without loss of accuracy. Clustering is used to reduce the number of instances used by the imputation. Feature selection is used to remove redundant and irrelevant features of training data which greatly reduces the cost of imputation. The paper also investigates the ability of Differential Evolution (DE) to search feature subsets with incomplete data. Results show that the integration of imputation, clustering and feature selection not only improves classification accuracy, but also dramatically reduces the computation time required to estimate missing values when classifying new instances. Highlights • Examine clustering and feature selection to improve classification with missing data. • Clustering speeds up imputation for classification with missing data. • Feature selection speeds up imputation and improves classification accuracy. [ABSTRACT FROM AUTHOR]

Abstract The paper reports an experimentally identified list of benchmark data sets that are hard for representative classification and feature selection methods. This was done after systematically evaluating a total of 48 combinations of methods, involving eight state-of-the-art classification algorithms and six commonly used feature selection methods, on 129 data sets from the UCI repository (some data sets with known high classification accuracy were excluded). In this paper, a data set for classification is called hard if none of the 48 combinations can achieve an AUC over 0.8 and none of them can achieve an F-Measure value over 0.8; it is called easy otherwise. A total of 15 out of the 129 data sets were found to be hard in that sense. This paper also compares the performance of different methods, and it produces rankings of classification methods, separately on the hard data sets and on the easy data sets. This paper is the first to rank methods separately for hard data sets and for easy data sets. It turns out that the classifier rankings resulting from our experiments are somehow different from those in the literature and hence they offer new insights on method selection. It should be noted that the Random Forest method remains to be the best in all groups of experiments. [ABSTRACT FROM AUTHOR]

The high dimensionality of data hinders the learning ability of machine learning algorithms. Feature selection techniques can be used to reduce dimensionality, which is an important step for processing high-dimensional data. Feature selection solves this problem by removing irrelevant and redundant information, which can improve learning models, reduce calculation time, and improve learning accuracy. In this paper, a novel filter in mixed-attribute datasets for feature selection is proposed. The independent attributes are mixed or heterogeneous in the sense that both numerical and categorical attribute types may appear together in the same dataset. Based on the preordonnances theory, we use a new concept to quantify the relevance and redundancy of features even if there are heterogeneous (mixed-type) data. The technique for order preference by similarity to the ideal solution is one of the well-known multicriteria decision-making methods; it is utilized as a weighting and informative feature selection filter. To assess the effectiveness of the proposed method, several experiments, both simulated and real, are performed, including a comparison to other well-known filter methods. The experimental results show that, in most cases, the method yielded competitive results in comparison to other methods. • Introducing new criterions for relevance and complementarity. • Dealing with mixed-attribute datasets without requiring a preprocessing step. • The multi-criteria decision method, namely TOPSIS, is used to score the explanatory features. • Investigate the performance of the proposed filter in high-dimensional data by using simulated and real data sets. [ABSTRACT FROM AUTHOR]

Phonocardiogram (PCG) signals contains valuable information pertaining to heart valve functionality, rendering them potentially useful for early detection of cardiovascular diseases. Automated classification of heart sounds harbors great promise for identifying cardiac pathologies. This paper introduces a novel automated approach to classify normal and abnormal heart sounds. Our methodology involves partitioning heart sounds into four segments: S1, S2, systolic, and diastolic, followed by extraction of time–frequency and time-statistical features. Prior to data classification, we employ two techniques - particle Swarm optimization (PSO) and Sequential Forward Feature Selection (SFFS) - for efficient feature selection. We assess the performance of the proposed method on the Physionet Challenge 2016 database, utilizing the 10-fold cross-validation method. To address the issue of dataset imbalance, we apply the synthetic minority over-sampling technique (SMOTE) to create balanced datasets. Our approach surpasses existing methods in the literature, as evidenced by its superior accuracy, sensitivity, and specificity metrics. Specifically, our method achieves an accuracy of 98.03%, a sensitivity of 97.64%, and a specificity of 98.43% in distinguishing normal from abnormal heart sounds on the Physionet database. These findings outperform the results obtained by previously established methods evaluated on the Physionet 2016 challenge database. [ABSTRACT FROM AUTHOR]

Neighborhood rough set theories are commonly used in global feature selection to achieve high performance in continuous data classification. However, selecting a single feature subset to represent the entire dataset may degrade the performance when there are intra-class dissimilarities among objects. Therefore, this paper proposes a novel feature-selection method, Granule-specific Feature Selection (GFS) to select local feature subsets for continuous data classification. The feature selection approach constitutes a novel feature selection algorithm and a novel feature evaluation function and uses existing approaches for granule identification and classification with some adjustments. The neighborhood rough set theories are used in granule (subclass) identification within each class when there are no subclass label information available in the training data, while an improved k-Nearest Neighbors approach is used in classification with granule-specific feature subsets. Experimental results show GFS outperforms most of the global, class-specific, and local feature selection baselines in terms of classification performance. • Granule-specific feature selection using neighborhood rough set theories. • Feature selection while managing intra-class dissimilarities of objects. • Handle data uncertainty of continuous data when selecting features. • Improve classification performance using granule-specific feature subsets. [ABSTRACT FROM AUTHOR]

Multi-label classification assigns multiple labels to each document concurrently. Many real-world classification problems tend to employ high-dimensional label spaces, which can be naturally structured in a hierarchy. In this type of problem, each instance may belong to multiple labels and labels are organized in a hierarchical structure. It presents a more complex problem than flat classification, given that the classification algorithm has to take into account hierarchical relationships between labels and be able to predict multiple labels for the same instance. Few studies have investigated multi-label text classification for the Arabic language. Most of these studies have focused mainly on flat classification and have neglected the hierarchical structure. Therefore, this paper explores the hierarchical multi-label classification in the context of the Arabic language. It proposes a hierarchical multi-label Arabic text classification (HMATC) model with a machine learning approach. The impact of feature selection methods and feature set dimensions on classification performance are also investigated. In addition, the Hierarchy Of Multilabel ClassifiER (HOMER) algorithm is optimized via examination of different sets of multi-label classifiers, clustering algorithms and different numbers of clusters to improve the hierarchical classification. Moreover, this study contributes to existing research by introducing a hierarchical multi-label Arabic dataset in an appropriate format for hierarchical classification and making it publicly available. The results reveal that the proposed model outperforms all models considered in the experiments in terms of the computational cost, which consumed less cost (2 h) compared with other evaluated models. In addition, it shows a significant improvement compared with the state-of-the-art model (Fatwa model) in terms of Hamming loss (0.004), hierarchical loss (1.723), multi-label accuracy (0.758), subset accuracy (0.292), micro-averaged precision (0.879), micro-averaged recall (0.828), and micro-averaged F-measure (0.853). [ABSTRACT FROM AUTHOR]

This paper proposes a method for diagnosing Parkinson's disease using ensemble classification of patient voice samples. Conducted research concerned testing the parameters of the ensemble of classifiers, in terms of types and numbers of classifiers included in it. More than a dozen popular classifiers were considered in the study. Additionally, for each of the tested classifiers, a set of features of voice samples were selected, for which a given classifier showed the highest classification efficiency. The Sequential Backward Selection (SBS), which belongs to the wrapper methods, was used for feature selection. The ensemble of classifiers was then tested in two cases, with all features considered and including only those indicated by the SBS method. The obtained results were compared with each other. All experiments were performed on a publicly available database containing voice samples of Parkinson's patients and healthy patients. This database can be found in the UCI archives. [ABSTRACT FROM AUTHOR]

The most common cancer type amongst women is the breast cancer with a large number of cases reported each year, many of them diagnosed in an advanced phase. In this paper our scope is to create a base for a support system that helps detecting breast cancer in an early stage. After defining the region of interest (ROI) and segmenting the result image (using k-means algorithm), Gray-Level Run-Length Matrices (GLRLM) features are extracted from both the ROI and from the segmented image in four directions (horizontal, vertical, first- and second diagonals). To reduce the dimensionality of the input data composed from the GLRLM features of the ROI and its segmentation for different combination of directions (removing redundant information, selecting just the most essential ones) two methods are used: Principal Component Analysis (PCA), and genetic algorithm (GA) feature selection. For classification, two methods are used and compared, namely Decision Trees (DT) and Random Forest (RF). For experiments we used the Mammographic Image Analysis Society (MIAS) dataset to train and to test the classifiers. The best performance is obtained for GLRLM features calculated for directions 45◦, and 90◦, using PCA feature selection and RF with a 100% training accuracy and 70% test accuracy. [ABSTRACT FROM AUTHOR]

Feature selection and extraction are the crucial steps that help to achieve meaningful classification of remotely sensed images. This paper presents a novel work, which selects a high level set of features from the remotely sensed images, than the conventional methods. The new features introduced in this work are inter-spectral and intra-spectral features. It is observed that these features aid us to differentiate between the characteristic pixels of each class in the image. Different classifiers are fed with different types of features of the images and a comparison of the same is also presented in the paper. [ABSTRACT FROM AUTHOR]

Cervical cancer is one of the vital and most frequent cancers, but can be cured effectively if diagnosed in the early stage. This is a novel effort towards effective characterization of cervix lesions from contrast enhanced CT-Scan images to provide a reliable and objective discrimination between benign and malignant lesions. Performance of such classification models mostly depends on features used to represent samples in a training dataset. Selection of optimal feature subset here is NP-hard; where, randomized algorithms do better. In this paper, Grey Wolf Optimizer (GWO), which is a population based meta-heuristic inspired by the leadership hierarchy and hunting mechanism of grey wolves has been utilized for feature selection. The traditional GWO is applicable for continuous single objective optimization problems. Since, feature selection is inherently multi-objective; this paper proposes two different approaches for multi-objective binary GWO algorithms. One is a scalarized approach to multi-objective GWO (MOGWO) and the other is a Non-dominated Sorting based GWO (NSGWO). These are used for wrapper based feature selection that selects optimal textural feature subset for improved classification of cervix lesions. For experiments, contrast enhanced CT-Scan (CECT) images of 62 patients have been used, where all lesions had been recommended for surgical biopsy by specialist. Gray-level co-occurrence matrix based texture features are extracted from two-level decomposition of wavelet coefficients of cervix regions extracted from CECT images. The results of proposed approaches are compared with mostly used meta-heuristics such as genetic algorithm (GA) and firefly algorithm (FA) for multi-objective optimization. With better diversification and intensification, GWO obtains Pareto solutions, which dominate the solutions obtained by GA and FA when assessed on the utilized cervix lesion cases. Cervix lesions are up to 91% accurately classified as benign and malignant with only five features selected by NSGWO. A two-tailed t -test was conducted by hypothesizing the mean F-score obtained by the proposed NSGWO method at significance level = 0.05. This confirms that NSGWO performs significantly better than other methods for the real cervix lesion dataset in hand. Further experiments were conducted on high dimensional microarray gene expression datasets collected online. The results demonstrate that the proposed method performs significantly better than other methods selecting relevant genes for high-dimensional, multi-category cancer diagnosis with an average of 12.82% improvement in F-score value. [ABSTRACT FROM AUTHOR]

In this paper we present the effect of four feature selection algorithms namely genetic algorithm, Forward feature selection, information gain and correlation based on four different classifiers (Decision tree C4.5, K-Nearest neighbors, neural network and support vector machine). The feature sets used in this paper are extracted features from the preprocessed songs using MIR Toolbox in MATLAB, which encompass rhythm based, timbre based, pitch based, tonality based and dynamic features. Feature vectors are extracted from music segments from first 30 seconds and last thirty seconds of the music signal (time-decomposition). Experiments were carried out on the three dominant genres of Indian music: Carnatic, Hindustani and Bollywood. Our dataset is small with 290 songs, trimmed to extract the first and the last 30 second percepts. As pure Carnatic and Hindustani music being more prevalent in traditional settings, have limited work done to make their digital copies available but the collection of music we have used consists of songs of some of the most profound singers contributing to each of these genres. For high-dimensional feature sets, the feature selection provides a compact but discriminative feature subset which has an interesting trade-off between classification accuracy and computational effort. The experimental results have shown that the common features selected by each of the feature selection algorithms with respect to classifiers and percentage of classification accuracies for all the classification algorithms. Furthermore, it can be observed from our experiment that information gain based feature selection gives better and consistent accuracies than other feature selection algorithms and Neural network and SVM classifiers are the best suited classifiers for Indian Song dataset. [ABSTRACT FROM AUTHOR]

The sentiment classification method is a research field that is proliferating in Indonesia since it is fast in extracting public opinion and provides essential and valuable information for stakeholders. Of the best-performing sentiment classification approaches, machine learning is one of them that has excellent performance. However, the method has several problems, such as noisy features and high dimensionality of features that significantly affect the sentiment classification performance. Therefore, to overcome the problems, this paper presents a novel feature selection using a combination of Query Expansion Ranking (QER) and Genetic Algorithm-Support Vector Machine (GA-SVM) for improving sentiment classification performance. Based on the experimental results, the proposed method could significantly improve sentiment classification performance, outperform all state-of-the-art algorithms, and decrease computational time. The method achieved the best performance in average precision, recall, and f-measure with the value of 96.78%, 96.76%, and 96.75%, respectively. [ABSTRACT FROM AUTHOR]

Compared with single fault, the occurrence and composition of coupling faults have more uncertainties and diversities, which make fault classification a challenging topic in academic research and industrial application areas. In this paper, the classification problems of coupling faults are addressed from a new perspective, which will provide diagnostic decisions for online operators to take immediate remedial measures to bring the abnormal operation back to an incontrol state. Specifically, the main innovations are: (1) a semisupervised classification scheme for coupling faults is first proposed, which combines adaptive classification with multi-task feature selection; (2) number of classifications can be learned adaptively and automatically; (3) common and specific features among single and the associated coupling faults can be captured, which are crucial for improving classification performance. A case study on hot rolling mill process is finally given to validate the effectiveness of the proposed scheme, and several competitive methods are employed to carry out the classification process. It can be observed that the obtained classification results for two different cases are more successful than the traditional methods. • To propose a semisupervised coupling fault classification method for hot rolling mill process. • To develop an adaptive classification approach for coupling faults. • To put forward a feature capture method among single and coupling faults. [ABSTRACT FROM AUTHOR]

Currently, the smartphone devices have become an essential part of our daily activities. Smartphone' users run various essential applications (such as banking and e-health Apps), which contains very confidential information (e.g., credit card number and its PIN). Typically, the smartphone's user authentication is achieved using mechanisms (password or security pattern) to verify the user identity. Although these mechanisms are cheap, simple, and quick enough for frequent logins, they are vulnerable to attacks such as shoulder surfing or smudge attack. This problem could be addressed by authenticating the users using their behaviour (i.e., touch behaviour) while using their smartphones. Such behaviours include finger's pressure, size, and pressure time while tapping keys. Selecting features (from these behaviours) could play an important role in the authentication process's performance. This paper aims to propose an efficient authentication method providing an implicit authentication for smartphone users while not imposing an additional cost of special hardware and addressing the limited smartphone capabilities. We first investigated feature selection techniques from the filter and wrapper approaches and then used the best one to propose our implicit authentication method. The random forest classifier is used to evaluate these techniques. It is also used to achieve the classification task in our authentication method. Using a public dataset, the experimental results showed that the filter-based technique (i.e., rank aggregation) is the best feature selection to build an implicit authentication method for the smartphone environment. It showed accuracy results around 97.80% using only 25 features out of 53 features (i.e., require less mobile resources (memory and processing power) to authenticate users. At the same time, the results showed that our method has less error rate: 2.03 FAR, 0.04 FRR, and 1.04 ERR, comparing to the related work. These promising results would be used to develop a mobile application that allows implicit authentication of legitimate owners while avoiding the traditional authentication problems and using fewer smartphone resources. [ABSTRACT FROM AUTHOR]

The increasing spread of monkeypox recently requires more study, investigation, and analysis. Not only to understand the different factors that contribute to the spread and transmission of the disease, but also to devise rules by which the disease can be diagnosed more accurately as the clinical presentation of monkeypox is very similar to that of smallpox. This paper presents a simple but effective strategy for diagnosing monkeypox, which is called Accurate Monkeypox Diagnosing Strategy (AMDS). The proposed AMDS consists of two successive phases, which are; (i) Feature Selection Phase (FSP) and (ii) Classification Phase (CP). During the FSP, the most beneficial features for diagnosing monkeypox are selected; afterwards the CP is applied during which the actual diagnosis takes place. The main contribution of AMDS relies on a proposed improvement of the Traditional Gray Wolf Optimization (TGWO), which is identified as Dynamic Recursive Gray Wolf Optimization (DRGW) and is applied in the FSP to select the most influential features. DRGW solves two of the most effective drawbacks of TGWO, which are; (i) inability to share good positions among wolves' pack and (ii) the lack of an accurate mechanism to locate the potential prey. AMDS has been experimentally evaluated compared to other competitors of recent feature selection techniques, in which CP has been employed using different well-known classifiers such as; Support Vector Machines (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Deep Neural Network (DNN). Experimental results conclude that AMDS promotes the diagnosing performance in terms of recall, accuracy, error, precision, and F score. This ensures that the proposed AMDS can operate efficiently in the disease diagnosis, which in turn ensures the effectiveness of the proposed DRGW. [ABSTRACT FROM AUTHOR]

Ordinal classification is a common classification problem, which widely exists in multi-attribute decision making problems. The dominance-based rough set approach (DRSA) is a knowledge acquisition tool for ordinal classification tasks. Nevertheless, the existence of noise information in the collected data greatly reduces the accuracy of the DRSA and some extended model. An effective way to solve this issue is to improve the robustness of these models. In addition, most existing feature selection strategies based on dominance rough sets mainly focus on the monotonic classification consistency between features and decision, but ignoring the classification information provided by features combination. Motivated by these two issues, this paper proposes a robust fuzzy dominance rough set model to combat noise interference and develop a feature selection approach based on the proposed model for ordinal classification tasks. First, a non-linear vague quantifier is adopted to construct the robust model, and related dependency function is introduced. Second, the rank entropy based uncertainty measures are explored to characterize the contribution of features combination for ordinal classification. Based on the proposed uncertainty measures, a new feature evaluation index is presented. Meanwhile, the corresponding feature selection algorithm is designed. Finally, numerical comparative experiments are performed to indicate that the proposed model and feature selection algorithm have good capability. • The fuzzy dominance rough set using a non-linear vague quantifier is proposed. • An uncertainty measure describing attribute combinations is proposed. • A classifier embedded feature selection algorithm is designed. • Experiments show the good performance of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Supervised and unsupervised classification is crucial in many areas where different types of data sets are common, such as biology, medicine, or industry, among others. A key consideration is that some units are more typical of the group they belong to than others. For this reason, fuzzy classification approaches are necessary. In this paper, a fuzzy supervised classification method, which is based on the construction of prototypes, is proposed. The method obtains the prototypes from an objective function that includes label information and a distance-based depth function. It works with any distance and it can deal with data sets of a wide nature variety. It can further be applied to data sets where the use of Euclidean distance is not suitable and to high-dimensional data (data sets in which the number of features p is larger than the number of observations n , often written as p > > n). In addition, the model can also cope with unsupervised classification, thus becoming an interesting alternative to other fuzzy clustering methods. With synthetic data sets along with high-dimensional real biomedical and industrial data sets, we demonstrate the good performance of the supervised and unsupervised fuzzy proposed procedures. • New fuzzy classification methodology based on the construction of prototypes • Distance-based, it overcomes the curse of dimensionality • It can be applied to a large spectrum of data, when the Euclidean distance is not suitable • It identifies K observations, selected between the deepest observations • Supervised and unsupervised approaches integrated in the objective function [ABSTRACT FROM AUTHOR]

The internet plays an important role in all areas of society from the economy to the government. Security means permitting things you do want, while preventing things you don’t want from performing. Computer security means storing the information on a computer in a secure manner. Nowadays Computer Security is pretentious by malicious data. Malware represents a serious threat to the security of computer systems. Traditional malware detection techniques like signature-based, heuristic-based, Specification-based detection are used to detect the known malware. These techniques detect the known malware accurately, but unable to detect the new, unknown malware. This paper presents a malware detection system based on the data mining and machine learning technique. The proposed method consists of disassemble process, feature extraction process and feature selection process. Three classification algorithms are employed on dataset to generate and train the classifiers named as Ripper, C4.5, IBk. The ensemble method i.e. Voting method is used to improve the accuracy of results. Here majority voting and veto voting are implemented the expected output is decided on the basis of majority and veto voting. The decision strategy of veto is improved by introducing the trust-based veto voting. This paper efficiently compared the results of majority voting, veto voting and trust-based veto voting. [ABSTRACT FROM AUTHOR]

Microarray dataset often contains a huge number of insignificant and irrelevant features that might lead to loss of useful information. The classes with both high relevance and having high significance feature sets are generally preferred for selecting the features, which determines the sample classification into their respective classes. This property has gained a lot of significance among the researchers and practitioners in DNA micro array classification. In this paper, Functional link neural network (FLNN) with four different basis functions named as Power series polynomial, Trigonometric, Chebyshev polynomial and Legendre polynomial functions have been considered to classify microarray data sets using t -test as a feature selection method. Further, this paper presents a comparative analysis on the obtained classification accuracy by coupling FLNN with different basis function and other existing models available in the literature. Performance parameters available in literature such as precision, recall, specificity, F-Measure, ROC curve and accuracy are applied in this comparative analysis to analyze the behavior of the classifiers. From the proposed approach, it is apparent that FLNN using Legendre polynomial is the suitable classification model among FLNN using different basis functions and other classifiers. [ABSTRACT FROM AUTHOR]

Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers’ attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection. [ABSTRACT FROM AUTHOR]

The paper presents application of data mining methods for recognizing the most significant genes and gene sequences (treated as features) stored in a dataset of gene expression microarray. The investigations are performed for autism data. Few chosen methods of feature selection have been applied and their results integrated in the final outcome. In this way we find the contents of small set of the most important genes associated with autism. They have been applied in the classification procedure aimed on recognition of autism from reference group members. The results of numerical experiments concerning selection of the most important genes and classification of the cases on the basis of the selected genes will be discussed. The main contribution of the paper is in developing the fusion system of the results of many selection approaches into the final set, most closely associated with autism. We have also proposed special procedure of estimating the number of highest rank genes used in classification procedure. [ABSTRACT FROM AUTHOR]

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles. [ABSTRACT FROM AUTHOR]

The paper presents a methodology of computer data analysis supporting medical diagnosis of obstructive sleep apnea (OSA) based on the results of polysomnography. Based on a database of 5114 patients, methods of detecting OSA with high accuracy have been developed. It has been also confirmed that obesity is an important risk factor. The methods of computer diagnostics have been compared with commonly used STOP-BANG questionnaire. The key stage of methodology referred to distinguished features that are most related to moderate or severe OSA presence, and are easy to gather at the same time. As a result of our studies we can conclude that it is possible to use smartwatch devices in order to develop a system of preliminary diagnostics of obstructive sleep apnea, which allows in the future for increased availability of apnea tests, reduced costs and earlier diagnosis. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study and compare some well-known and commonly used methods for sentiment analysis to evaluate the opinion and emotion expressed in Algerian texts. The classification task herein is a ternary sentiment classification. By using several combinations of text preprocessing and data representation techniques, we aim to compare the precise modelling results of Deep Learning models with other commonly used algorithms (random forest, maximum entropy, SVM, and the lexicon-based method for which we tested several lexicons). Based on the experiments carried out, Deep Learning models clearly outperform the baseline and offer better accuracy especially for CNN. In order to improve modelling results, we set a new baseline for future works. This is the integrated embeddings in the training model. We experimented with different models and data representations, including a recent approach, the " contextual embedding" which appeared in 2018 and gained popularity in the NLP community in 2019. Our results give openings for further research in this domain. [ABSTRACT FROM AUTHOR]

Discretisation often constitutes a part of initial data preparation stage. It translates continuous domain of features into granular, by assigning a number of intervals to represent attributes' values by nominal categories. Typically all real-valued features are subjected to transformations, regardless of their characteristics. The paper presents research on discretisation executed with a discerning approach. To all available attributes, feature selection mechanisms were employed, in the form of rankings that order variables based on their importance. Exploiting this discovered knowledge on attributes, discretisation was then driven by a ranking, and either highest or lowest ranking features were selected for transformation. The influence of selective discretisation on the performance of classification systems was studied for three popular inducers. The procedure was employed in the field of stylometry, and a task of authorship recognition, considered as a binary classification with balanced classes. The experiments show that discretisation based on importance of features can lead to better performance than in the case of transformations applied to all attributes. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. This paper tries to improve the structure of basic SSA to enhance solution accuracy, reliability and convergence speed. A new control parameter, inertia weight, is added to adjust the present best solution. The new method known as improved salp swarm algorithm (ISSA) is tested in feature selection task. The ISSA algorithm is consolidated with the K-nearest neighbor classier for feature selection in which twenty-three UCI datasets are utilized to assess the performance of ISSA algorithm. The ISSA is compared with the basic SSA and four other swarm methods. The results demonstrated that the proposed method produced superior results than the other optimizers in terms of classification accuracy and feature reduction. [ABSTRACT FROM AUTHOR]

In text mining, feature selection (FS) is a common method for reducing the huge number of the space features and improving the accuracy of classification. In this paper, we propose an improved method for Arabic text classification that employs the Chi-square feature selection (referred to, hereafter, as ImpCHI) to enhance the classification performance. Besides, we have also compared this improved chi-square with three traditional features selection metrics namely mutual information, information gain and Chi-square. Building on our previous work, we extend the current work to assess the method in terms of other evaluation methods using SVM classifier. For this purpose, a dataset of 5070 Arabic documents are classified into six independently classes. In terms of performance, the experimental findings show that combining ImpCHI method and SVM classifier outperforms other combinations in terms of precision, recall and f-measures. This combination significantly improves the performance of Arabic text classification model. The best f-measures obtained for this model is 90.50%, when the number of features is 900. [ABSTRACT FROM AUTHOR]

Today's health care is very important aspect for every human, so there is a need to provide medical services that are easily available to everyone. In this paper, the main focus is to predict the liver disease based on a software engineering approach using classification and feature selection technique. The implementation of proposed work is done on Indian Liver Patient Dataset (ILPD) from the University of California, Irvine database. The different attributes like age, direct bilirubin, gender, total bilirubin, Alkphos, sgpt, albumin, globulin ratio and sgot etc, of the liver patient dataset, are used to predict the liver diseases risk level. The various classification algorithms such as Logistic Regression, SMO, Random Forest algorithm, Naive Bayes, J48 and k-nearest neighbor (IBk) are implemented on the Liver Patient dataset to find the accuracy. The comparison different classifier results are done of feature selection and without using feature selection technique. The development of intelligent liver disease prediction software (ILDPS) is done by using feature selection and classification prediction techniques based on software engineering model. [ABSTRACT FROM AUTHOR]

The lung sounds produced by a human convey valuable information about the health of the respiratory system, and these signals are complex in nature. In this paper, a study was conducted to find the importance of feature selection from these signals for the purpose of classification. Feature selection is performed using two different approaches: RSFS and SFS. The experiment was conducted on a dataset of 85 samples using the (SVM, KNN, and Naïve Bayes) classifiers. The computational results obtained are promising, and the proposed feature selection techniques show better performances in terms of Precision, Recall, and F-Measures. [ABSTRACT FROM AUTHOR]

• HPSO-SSM is proposed based on original particle swarm optimization. • A new wrapper-based feature selection approach based on HPSO-SSM is proposed. • The logistic map sequence is used to enhance the diversity in the search process. • An innovative position update model is presented to improve the position quality. • Our method outperforms seventeen extremely competitive methods in terms of accuracy. The "curse of dimensionality" is one of the largest problems that influences the quality of the optimization process in most data mining, pattern recognition, and machine learning tasks. Using high-dimensional datasets to train a classification model may reduce the generalization performance of the learned model. In addition, high dimensionality of the dataset results in high computational and memory costs. Feature selection is an important data preprocessing approach in many practical application domains that are relevant to expert and intelligent systems. Feature selection aims at selecting a subset of informative and relevant features from an original feature dataset. Therefore, using a feature selection approach to process the original data prior to the learning process is essential for enhancing the performance on the classification task. In this paper, hybrid particle swarm optimization with a spiral-shaped mechanism (HPSO-SSM) is proposed for selecting the optimal feature subset for classification via a wrapper-based approach. In HPSO-SSM, we make three improvements: First, a logistic map sequence is used to enhance the diversity in the search process. Second, two new parameters are introduced into the original position update formula, which can effectively improve the position quality of the next generation. Finally, a spiral-shaped mechanism is adopted as a local search operator around the known optimal solution region. For a complete evaluation, the proposed HPSO-SSM method is compared with six state-of-the-art meta-heuristic optimization algorithms, ten well-known wrapper-based feature selection techniques, and six classic filter-based feature selection methods. Various assessment indicators are used to properly evaluate and compare the performances of these approaches on twenty classic benchmark classification datasets from the UCI machine learning repository. According to the experimental results and statistical tests, the developed methods effectively and efficiently improve the classification accuracy compared with other wrapper-based approaches and filter-based approaches. The results demonstrate the high performance of the HPSO-SSM method in searching the feasible feature space and selecting the most informative attributes for solving classification problems. Therefore, the HPSO-SSM method has broad application prospects as a new feature selection approach. [ABSTRACT FROM AUTHOR]

With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine multiple view data of the same entity for data classification, thereby improving learning performance. Previous multi-view research methods mainly concentrate on the relationship between different data views for classification problems. However, when the data is in high dimensions, it is necessary to perform feature selection in the multi-view data classification process. In this paper, we proposed a Multi-view Support Vector Machine Classification with Feature Selection (MSVMCFS) algorithm, which can not only classify multi-view data, but also select features for each view data in the process of classification. In the model, feature selection is performed by the l 1 norm sparsity regularization, and consistency and complementarity between the two views are maintained. To achieve the optimization goal, we adopt linear programming to solve the model. The experimental results on 30 binary datasets demonstrate the validity of the model. [ABSTRACT FROM AUTHOR]

Medicine is one of the sciences where development of computer science enables a lot of improvements. Usage of computers in medicine increases the accuracy and speeds up processes of data analysis and setting the diagnoses. Nowadays, numerous computer aided diagnostic systems exist and machine learning algorithms have significant role in them. Faster and more accurate systems are necessary. Common machine learning task that is part of computer aided diagnostic systems and different medical data analytic software packages is classification. In order to obtain better classification accuracy it is important to choose feature set and proper parameters for the classification model. Medical datasets often have large feature sets where many features are in correlation with others thus it is important to reduce the feature set. In this paper we propose adjusted brain storm optimization algorithm for feature selection in medical datasets. Classification was done by support vector machine where its parameters are optimized also by brain storm optimization algorithm. The proposed method is tested on standard publicly available medical datasets and compared to other state-of-the-art methods. By analyzing the obtained results it was shown that the proposed method achieves higher accuracy and reduce the number of feature needed. [ABSTRACT FROM AUTHOR]

Time series classification exists in widespread domains such as EEG/ECG classification, device anomaly detection, and speaker authentication. Although many methods have been proposed, efficient selection of intuitive temporal features to accurately classify time series remains challenging. Therefore, this paper presents TSC-RTF, a new time series classification method using random temporal features. First, to ensure the intuitiveness of the features, TSC-RTF selects subsequences containing important data points as candidates for intuitive temporal features. Then, TSC-RTF uses random sampling to reduce the number of candidates significantly. Next, TSC-RTF selects the final temporal features using a random forest to ensure the validity of the final temporal features. Finally, a deep learning classifier is trained by TSC-RTF to achieve high accuracy. The experimental results show that the proposed method can compete with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In recent years, the use of frequent temporal patterns as features for classification has increasingly been used and investigated. In this process, commonly frequent patterns are mined from each class separately. Then the patterns are unified, and feature selection methods may be employed, which are given to induce a classifier. However, this approach is very time consuming since the mining of each class separately takes time. In this paper, we introduce the Saraswati suite that can modify a temporal patterns discovery algorithm into a predictive temporal patterns discovery algorithm, which we demonstrate on Time Intervals Related Patterns. The suite enables predictive patterns to be favored in runtime, while mining both classes simultaneously to discover these patterns. This is through the use of a novel stopping criteria that we call the Saraswati selection criteria and strategies suite. Since the selection criteria are based on the patterns' metrics, such as their frequency in each class or their reoccurrence, and more, it is explainable to domain experts, rather than as a score as happens with common feature selection measures. We modified an existing time intervals related patterns discovery algorithm according to the Saraswati suite, and evaluated it rigorously against the current approach on six real-life datasets. Our results show that the Saraswati-based algorithm is much faster than discovery of the entire set of frequent patterns, and the selection criteria are more effective than existing state-of-the-art feature selection methods when the discovered predictive patterns are used for classification. Additionally, the selection of the patterns is explainable in the domain expert's terminology based on several meaningful metrics. [ABSTRACT FROM AUTHOR]

This paper proposes a method using statistical local and global features for classifying healthy and murmur heart sound recordings from phonocardiogram signals. Classification requires features extraction step that converts each signal into a sequence of feature vectors composed of static and dynamic energy coefficients computed from overlapped analysis windows. Firstly, we propose, for each heartbeat, to extract local features from the local consecutive regions (1st Sound, Systole, 2nd Sound, Diastole) and global ones from the global region. For each region, the features are the statistical features (mean and standard deviation) computed on the feature vector sequence plus the duration. Secondly, we propose to select the relevant features using filter approach based on mutual information criteria. The extraction and selection methods are validated using K nearest neighbor and Gaussian Mixture Models as classifiers. The classification system were evaluated on a sub-dataset of the public PASCAL heart sounds classifying challenge. Results showed that 12 features selected using the Max-Relevance Min-Redundancy selection strategy were sufficient to explain the two classes with 94.97% classification rate higher than 92.74% state-of-the-art rate. We also showed this selection strategy helped the system to be robust to the testing phase when using automatic segmentation rather than manual segmentation. This work demonstrates that local systolic segment features are the most relevant for murmur/normal classification, regardless of segmentation methods. It also shows that feature selection algorithms have potential to highlight certain relevant regions in signals, which is useful for aided diagnostic systems and basic research. [Display omitted] • Local and global statistical features are extracted from PCG beat signals. • Local systolic segment features have a major impact on normal/murmur classification. • "Filters" strategy algorithms select local features rather than global features. • The importance of systolic region is highlighted for normal/murmur classification. [ABSTRACT FROM AUTHOR]