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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]

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]

• 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]

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]

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]

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]

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]

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]

Polycystic Ovary syndrome (PCOS) is the most common serious health problem in women worldwide. It affects the women's fertility and may lead to various health problems like type 2 diabetes, cardiovascular diseases, etc. Early diagnosis of PCOS is recommended for proper treatment. In this paper, we propose SmS: SMOTE-Stacked hybrid model, for the early diagnosis of PCOS. In the proposed model, we combine the ensemble technique 'stacking' with oversampling technique 'SMOTE' i.e., Synthetic minority oversampling technique. In the Stacking ensemble technique, training is performed at two levels: base level and meta level. We use six classifiers Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and AdaBoost (AdaB) as base leaners at the base level and consider each of these classifiers separately at the meta -level to implement SmS hybrid models: Stack-LR, Stack-SVM, Stack-DT, Stack-RF, Stack-NB and Stack-AdaB. Standardization, SMOTE and Backward Feature Elimination (BFE) are performed before training the classifiers. We evaluate the performance of the individual classifiers and hybrid models on PCOS dataset (Imbalanced) taken form the Kaggle Repository and observe that Stack-AdaB shows the most promising results. [ABSTRACT FROM AUTHOR]

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. Existing methods concentrate on the classification information from candidate features while seldom considering the changing information supported by selected features. In this paper, we construct a dynamic support ratio (DSR), which employs the new information of selected features to support classification. DSR explicitly describes the dynamic interactions between selected features and candidate features. Simultaneously, the feature relevance and feature redundancy are treated adaptively. Thus, distinctive features can be noticed sensitively. Afterward, a novel feature selection method based on a dynamic support ratio (DSRFS) is proposed. The proposed method is established on 18 benchmark data sets with four different classifiers. Classification accuracy, standard deviation, recall and statistical validations are employed to measure the classification performance. Extensive experiments demonstrate that DSRFS not only reduces the dimension of the feature space effectively, but also obtains the best average classification accuracy. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading killers of women around the world. Finding compounds with good bioactivity, metabolic dynamics and safety, including Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET for short), is a long and challenging task in breast cancer therapy. In the paper, molecular descriptor data of compounds was analyzed by the ensemble learning algorithm, and important features were selected for the development and validation of ADMET classification models. The overall process includes data cleaning, data splitting to training and testing sets, feature selection and classification model evaluation. A Two-Level Stacking Algorithm (TLSA) based on ensemble learning is proposed for ADMET classification. Various performance measures like classification accuracy, precision, recall, confusion matrix, F1-score, Receiver Operating Characteristic (ROC) curves and the Area Under the ROC Curves (AUC) are reported to show the superiority of the proposed method as compared to different classifiers. The experimental results show that the second level algorithm for TLSA utilizes Logistic Regression is better than other classifiers for the properties of Absorption, Distribution and Excretion, with accuracy of 94.6037%, 94.9410% and 88.1956% respectively. For the properties Metabolism and Toxicity, the second level algorithm utilizes Support Vector Machine to achieve the best classification performance, with accuracy of 88.8702% and 96.7960% respectively. The results show that the proposed approach works well with the classification of compound properties and can be a good alternative for the well-known machine learning program. • A novel TLSA model for ADMET classification of compounds is proposed. • The proposed method improves the accuracy of classification results. • Applying a wide range of evaluation metrics to ensure the model's reliability. • Most important molecular descriptors of compounds were selected using RF. [ABSTRACT FROM AUTHOR]

Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia which is often challenging to diagnose because patients may be asymptomatic, and episodes are usually intermittent. In this paper we describe a method for identifying patients with a history of PAF using electrocardiogram (ECG) recordings during sinus rhythm. We analyzed, on a beat-to-beat basis, the P-waves in the sinus rhythm ECGs of 69 patients with a history of PAF and 59 healthy individuals. From each subject's P-waves, we calculated key electrocardiographic metrics, including some which are proposed here for the first time. Using means testing and feature selection methods, we discerned the features which were most useful for classification, and trained a Random Forest which identified PAF patients. Our approach achieved a 93.45% accuracy, sensitivity of 95.21%, and specificity of 91.40% using P-wave integral and novel amplitude and slope-based features which ranked highest in importance compared to other metrics from the literature. In particular, descriptive statistics of P-wave amplitudes, slopes, and integrals, were effective for identifying subjects with PAF history vs. healthy individuals. Our method has a high sensitivity and discrimination ability, with an AUC=0.9669 which is superior to others', and can thus be potentially valuable for the early identification of patients who are prone to episodes of PAF, even as part of the standard cardiological checkup that most adults undergo periodically. • Early identification of PAF during sinus rhythm with high accuracy. • P-wave signal metrics on a beat-to-beat basis are proposed. • P-wave amplitude, integral and slope features were ranked as the most important. • A RF classifier distinguishes PAF from healthy subjects with a state-of-the-art AUC. [ABSTRACT FROM AUTHOR]

Feature selection techniques have been presented to allow us to choose a small subset of the original components' relevant features by removing irrelevant or redundant features. Feature selection is essential for many reasons such as simplification, performance, computational efficiency, and quality interpretability. Owing to the importance mentioned above, many researchers have proposed and developed many algorithms to solve the feature selection problem. Although these approaches produce useful results, they possess some shortcomings like inadequate feature reduction. In this paper, a novel feature selection algorithm based on the crow search algorithm is presented. The algorithm uses dynamic awareness probability to keep the balance between the local and global search processes. Moreover, a novel neighborhood assigning strategy has been introduced to optimize the local search. Considering the best-selected features in each iteration helps attain more benefits in global search. The main superiority of the proposed algorithm is the significant feature reduction along with retaining the accuracy. Compared to enhanced crow search algorithm, the proposed algorithm has improved the feature reduction metric and fitness metric by 27.12% and 5.16%, respectively, while losing the accuracy metric by only 0.53%. Several popular UCI datasets have been employed to evaluate the proposed feature selection algorithm. The experimental results show that the proposed algorithm outperformed other feature selection algorithms in state-of-the-art related works regarding feature reduction and accuracy. • Improve the balance between the local and global search. • Introducing a new neighborhood concept for improving the local search. • Proposing a new method to search more purposeful during exploration. • Increasing convergence rate using chaos. • Being pioneer in reducing the dataset volume. [ABSTRACT FROM AUTHOR]

An intrusion detection system (IDS) is considered critical for detecting threats, intrusions, and unauthorized access. IDS monitors massive network traffic that includes irrelevant and extravagant features that profoundly impact the system's efficiency and slow down the classification process for accurate decisions. Its effectiveness is tested over the various techniques that comprise an enormous volume of data and heavy network traffic. Many approaches, such as machine learning algorithms, data mining, swarm intelligence, and artificial neural networks (ANN), have been implemented for adequate and improved IDSs. This paper recommends a novel feature selection method using a genetic algorithm (GA) that determines the optimal feature subsets from the NSL-KDD dataset. Further, hybrid classification has been performed using logistic regression (LR) and decision tree (DT) to achieve a better detection rate (DR) and accuracy (ACC). This research applied and compared several meta-heuristic algorithms' performance to optimize the selected optimal features. The experimental results show that the grey wolf optimization (GWO) algorithm gives the best accuracy of 99.44% and DR of 99.36% with the reduction of features (=20) out of (=41). The results of the proposed work are compared with the existing feature selection methods to verify improved performance. [Display omitted] • Novel feature selection method using a genetic algorithm (GA) that determines the optimal feature subsets. • Hybrid classification to achieve a better detection rate (DR) and accuracy (ACC). • Compared several meta-heuristic algorithms' performance to optimize the selected optimal features. • The grey wolf optimization (GWO) algorithm gives the best accuracy of 99.44% and DR of 99.36% with the reduction of features (=20) out of (=41). • Optimization for improvement of IDS. [ABSTRACT FROM AUTHOR]

Feature selection, which aims to screen out redundant and irrelevant features from datasets, is integral to machine learning and data mining. Grey Wolf Optimization (GWO) is a recent meta-heuristic algorithm based on swarm intelligence and has wide applicability to various optimization problems due to its fast convergence and few parameters. However, since the wolf pack is always dominated by the three leading wolves (i.e. , α , β and δ), the GWO algorithm suffers from weak exploration throughout the whole optimization process and easily stagnates into local optima. In this paper, an Adaptively Balanced Grey Wolf Optimization (ABGWO) algorithm is proposed to seek out the optimal feature subset for high-dimensional classification. Specifically, to improve the exploration ability of GWO, a random wolf is introduced to cooperate with α , β and δ. A novel level-based strategy is further adopted to select the random wolf. Besides, to dynamically modulate the exploration and exploitation ability in different optimization stages, an adaptive coefficient is introduced to regulate the leadership of α , β , δ and the randomly-selected wolf. Finally, the improvement of exploration and exploitation is validated on 12 high-dimensional datasets provided by Arizona State University and University of California Irvine, and the superiority of ABGWO is further verified by comparing it with seven state-of-the-art feature selection approaches on the aspect of classification accuracy, size of feature subset and computational time. • Introducing a random individual to prevent sharp decline in population diversity. • Semi-random selection prevents the rapid decrease of the convergence speed. • A coefficient factor linearly increases to adaptively adjust the exploration and exploitation. • The improved GWO algorithm for high-dimensional feature selection significantly boosts the classification performance. [ABSTRACT FROM AUTHOR]

There is a growing interest in developing feature subset selection schemes for high-dimensional datasets by filter, wrapper, embedded, and hybrid manners. In this paper, we propose a new hybrid (filter-wrapper) feature selection approach. At first, in the filter step, we rank input features according to their relevance with the class label. Afterwards, we apply different clustering methods for the classification of the selected features. We perform an inner and outer cluster ranking based on the primary feature ranking in the next step. Then, different search strategies are performed on the best cluster of features in the wrapper phase. Moreover, we add some of them to the feature set based on the classifiers (nearest neighbor, decision tree, support vector machine, and random forests) feedback. Then, the algorithm goes to the next cluster, and this process is continued till all clusters are met. Finally, we compare the results of the proposed method to the state-of-the-art schemes. Comparison results imply the superiority of the proposed method to the counterparts on eight high-dimensional datasets in terms of accuracy and computational complexity. • A new hybrid feature selection framework named the HyCluster is proposed. • Our algorithm significantly improves the time complexity using feature clustering. • It selects relevant and irredundant features and significantly improves accuracy rates. • Comparison results imply the superiority of the proposed method to the counterparts. [ABSTRACT FROM AUTHOR]

• An Enhanced Binary Bat Algorithm (EBBA) for classification of fetal state has been conferred. • Proposed EBBA selects the most discriminative features. • The recommended algorithm has been employed on the cardiotocography dataset with the average accuracy of 96.21%. • Three different machine learning classifiers: (i) DecisionTree (ii) K-Nearest Neighbor (k-NN) (iii) Random Forest has been used to assess the worthiness of optimal features selected by EBBA • Comparison of various evolutionary algorithms like EBBA, qGWO, Genetic in terms of features selected and accuracies obtained. • The proposed EBBA is presented in as simple, forthright and coherent manner as conceivable. The timely assessment of hypoxic fetuses by cardiotocography is of utmost significance as deficiency of oxygen in fetuses' leads to fetal distress which can further prove to be fatal or cause neurological diseases. This paper puts forward an Enhanced Binary Bat algorithm, the altered form of Binary Bat Algorithm for the multi-classification problem of Cardiotocography. Subset of optimal and relevant features is selected using the optimized and Enhanced BBA algorithm from cardiotocography dataset. Features selected by various evolutionary algorithms EBBA (Enhanced Binary Bat algorithm), qGWO (quantum Grey Wolf Optimization), Genetic algorithm are 11, 15, 12 simultaneously. EBBA efficiently selects most reduced set of features. The proposed EBBA can be used in feature selection and classification of cardiotocography dataset under different fetus state i.e. normal, suspect and pathologic with an accuracy of 96.21% using machine learning classifier i.e. random forest. [ABSTRACT FROM AUTHOR]

The complexity and accuracy of classification algorithms largely depend on the size and the quality of the feature set used to build classifiers. Feature evaluation and selection are critical steps to decide a small set of high-quality features to build accurate and efficient classifiers since low-quality features not only have negative impacts on classification results but also increase the complexity of classification algorithms. Current popular feature selection algorithms are not sufficient in selecting a set of high-quality features and discarding low-quality features, especially for streaming data. This paper proposes a novel and efficient approach, optimal feature evaluation and selection (OFES), to evaluate and select high-quality features for multi-class classification. OFES first measures the difference between any two classes based on the feature that is to be evaluated. Then, it defines two quantitative measures to evaluate quality of the feature and identify high-quality features. Applying OFES in a multi-class classification application that identifies users based on their arm movement patterns, we find when compared with other popular feature evaluation and selection approaches, such as Information Gain Feature Ranking and Random Projections with Matlab feature ranking, OFES identifies a set of high-quality features that improves the accuracy of classification regardless of different classification algorithms. It also demonstrates great scalability with the increase of number of classes and yields a higher accuracy of 95%. [ABSTRACT FROM AUTHOR]

Today, a significant number of biomedical data is generated continuously from various biomedical equipment and experiments due to rapid technological improvements in medical sciences. Effective biomedical data analysis, such as extracting biological and diagnostically significant features, is a very challenging task. This paper proposes hybrid Machine Learning Classification Techniques based on ensemble technique with Enhanced-Grey Wolf Optimization (E-GWO) feature selection algorithm to analyze these complex biomedical data. We combined five biomedical heart disease data sets for the experimental work: Cleveland, Long-Beach-VA, Switzerland, Hungarians, and Statlog. New hybrid Machine Learning Classification Techniques classifiers like Naive Bayes Bagging Technique (NBBT), Random Forest Bagging Technique (RFBT), Decision Tree Bagging Technique (DTBT), K-Nearest Neighbors Bagging Technique (KNNBT), Neural Network Bagging Technique (NNBT), Gradient Boosting Boosting Technique (GBBT), and Adaptive Boosting Boosting Technique (ABBT) are developed with bagging and boosting methods. Accuracy, Recall, Precision, F1-score, Specificity, Error Rate, G-mean, False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) are used to evaluate hybrid techniques. The experimental result shows that the developed hybrid classifier RFBT achieves the highest accuracy of 99.26% with E-GWO feature selection. The proposed method has 11.90% improved the accuracy to the conventional model. [ABSTRACT FROM AUTHOR]

In recent years, brain storm optimization (BSO) algorithm has received much attention in solving classical optimization problems and is used to implement evolutionary classification models. However, in practical applications, large-scale datasets complicate the structure of the classification model, which can have a great impact on the classification performance. In the optimization process, the traditional single-strategy BSO cannot preserve the information of dominant solution well, and its generation strategy is inefficient in solving various complex practical problems. To solve this problem, we introduce feature selection to improve the optimization model structure. Meanwhile, in order to enhance the search capability of BSO, three new generation strategy are embedded in the BSO algorithm in this paper. With the three generation methods of global optimal, local optimal and nearest neighbor, the information of the dominant solution can be better preserved and the search efficiency can be improved. The performance of the proposed generation strategy in solving classification problems is demonstrated on ten datasets with different sizes and dimensions. The experimental results reveal that the new generation strategy can enhance the performance of BSO algorithm for solving classification problems. [ABSTRACT FROM AUTHOR]

In this paper we propose three novel feature ranking methods for supervised feature selection in the context of classification which are based on possibility theory. All three methods – nonspecificity, strife and total uncertainty – are tested on eight artificial data sets and ten medical real-world data sets and benchmarked against ReliefF, the Fisher score, the fuzzy entropy and similarity (FES), the Fuzzy similarity and entropy (FSAE) filter, symmetrical uncertainty as well as using no feature selection. The feature ranking methods were applied following two approaches: (1) using a fixed threshold for the number of highest-ranking features selected and (2) using a hybrid feature selection approach with a classifier (k-nearest neighbor classifier, decision tree, similarity classifier, SVM) to select the optimal number of features to select. The results indicate that strife and the Fisher score are the two feature ranking methods that for both approaches are on average ranked the highest in terms of the test set accuracy on the real-world data sets. Besides that, for the hybrid approach, strife uses most of the time a considerably smaller number of features than nonspecificity and total uncertainty. In terms of stability, which was measured with the adjusted stability measure (ASM), the Fisher score and strife were among the most stable feature ranking methods in this study. Additionally, strife's feature subsets were diverse compared to those of the remaining feature selection methods, making it a good candidate to be included in a feature selection ensemble. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm (SCA) that is one of the population-based metaheuristic optimization algorithms basically consists of the updating mechanism based on sine and cosine functions. In this algorithm, a few random and adaptive variables are also utilized for more effective motions of the candidate solutions. SCA has some drawbacks like other some metaheuristic algorithms. SCA tends to be stuck into the local regions in the search space and this affects negatively on the computational effort required to find the best solution point in the search space. This paper presents four different improved versions of SCA. The proposed improvements on original SCA are the innovations on the updating mechanism of SCA. To evaluate the performances of Improved Sine Cosine Algorithms (ImpSCAs), well-known numerical optimization problems including CEC 2014 test suite are used. Firstly, different analyses of the proposed ImpSCAs are dealt with such as the convergence analysis, search history analysis, trajectory analysis, average distance analysis, and computational complexity analysis. Secondly, the proposed four versions of ImpSCAs are compared with the original SCA for CEC 2014 benchmark problems with dimension sizes of 10D, 30D and 50D. Finally, original SCA and ImpSCAs are adapted to select optimal feature combination and they are tested for 10 feature selection datasets taken from the UCI machine learning repository. The benchmark results show that the performances of the ImpSCA 1 , ImpSCA 2 , and ImpSCA 4 are better than that of the original SCA. From the feature selection results, it is observed that three versions of ImpSCAs (except ImpSCA 3) outperform the original SCA in 80% of the datasets. Source codes of ImpSCAs are publicly available at https://github.com/uguryuzgec/ImpSCAs. • We focused to improve the exploration and exploitation ability of the original SCA. • The basic idea is based on the innovations on the update mechanism. • The ISCAs were evaluated using CEC2014 benchmark test suite. • The proposed ISCAs were tested for 10 popular UCI datasets. • Three versions of ISCAs (except ISCA 3) outperform the SCA in 80% of the datasets. [ABSTRACT FROM AUTHOR]

• A new model to classify data based on Support vector machines is introduced. • The model deals with outlier detection and feature selection simultaneously. • Strategies for initializing the big M parameters of the model are developed. • An heuristic algorithm based on kernel search to compute the classifier is presented. • The classification performance of the model is tested on real-life datasets. This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic approach is validated by comparing the quality of the solutions provided by this approach with the exact approach. In addition, the classifiers obtained with the heuristic method are tested and compared with existing SVM-based models to demonstrate their efficiency. [ABSTRACT FROM AUTHOR]

• Feature selection improves performance of machine learning algorithms. • Feature selection with more n-tier techniques is simpler and more stable. • A feature selection model that is not specific to any data set is widely applied. Feature Selection and classification have previously been widely applied in various areas like business, medical and media fields. High dimensionality in datasets is one of the main challenges that has been experienced in classifying data, data mining and sentiment analysis. Irrelevant and redundant attributes have also had a negative impact on the complexity and operation of algorithms for classifying data. Consequently, the algorithms record poor results or performance. Some existing work use all attributes for classification, some of which are insignificant for the task, thereby leading to poor performance. This paper therefore develops a hybrid filter model for feature selection based on principal component analysis and information gain. The hybrid model is then applied to support classification using machine learning techniques e.g. the Naïve Bayes technique. Experimental results demonstrate that the hybrid filter model reduces data dimensions, selects appropriate feature sets, and reduces training time, hence providing better classification performance as measured by accuracy, precision and recall.. [ABSTRACT FROM AUTHOR]

• The study proposes a novel method of using inertial sensors for game play analysis. • Data from accelerometer and gyroscope is used for expert-novice classification. • Early and late fusion of features is evaluated along with wrapper feature selection. • State-of-the-art classification accuracy is achieved using smartphone inertial data. The gaming industry has seen a tremendous growth in the last decade due to an exponential increase in the number of smartphone users. Embedded smartphone sensors provide solutions for automatic game controls during game-play. In this paper, we present an experimental study for the expertise classification of a game (mobile-based) player using smartphone inertial sensors, while they are simultaneously used for game controls. The game expertise level of participants is either labeled as expert or novice using game scores. Towards this end, data from 38 participants are curated during Traffic Racer game-play (in three different trials) using the embedded gyroscope and accelerometer sensors of the smartphone. These signals are pre-processed using Savitzky-Golay smoothing filter to remove noise. Twenty time domain features are extracted from the pre-processed data and are subjected to the wrapper-based feature selection method to select an optimum subset of features. Three classifiers, including k-nearest neighbor (k-NN), random forest, and the Naive Bayes, are evaluated towards the classification of player's expertise level, i.e., expert and novice. The best average accuracy of 92.1 % is achieved with k-NN classifier using the fusion of gyroscope and accelerometer data, which outperforms the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]