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• 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]

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]

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]

Feature selection is very important for pattern recognition to reduce the dimensions of data and to improve the efficiency of learning algorithms. Recent research on new approaches has focused mostly on improving accuracy and reducing computing time. This paper presents a flexible feature-selection method based on an optimized kernel mutual information (OKMI) approach. Mutual information (MI) has been applied successfully in decision trees to rank variables; its aim is to connect class labels with the distribution of experimental data. The use of MI removes irrelevant features and decreases redundant features. However, MI is usually less robust when the data distribution is not centralized. To overcome this problem, we propose to use the OKMI approach, which combines MI and a kernel function. This approach may be used for feature selection with nonlinear models by defining kernels for feature vectors and class-label vectors. By optimizing the objection equations, we develop a new feature-selection algorithm that combines both MI and kernel learning, we discuss the relationship among various kernel-selection methods. Experiments were conducted to compare the new technique applied to various data sets with other methods, and in each case the OKMI approach performs better than the other methods in terms of feature-classification accuracy and computing time. OKMI method solves the problem of computation complexity in the probability of distribution, and avoids this problem by finding the optimal features at very low computational cost. As a result, the OKMI method with the proposed algorithm is effective and robust over a wide range of real applications on expert systems. [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]

With the advent of Big Data, data is being collected at an unprecedented fast pace, and it needs to be processed in a short time. To deal with data streams that flow continuously, classical batch learning algorithms cannot be applied and it is necessary to employ online approaches. Online learning consists of continuously revising and refining a model by incorporating new data as they arrive, and it allows important problems such as concept drift or management of extremely high-dimensional datasets to be solved. In this paper, we present a unified pipeline for online learning which covers online discretization, feature selection and classification. Three classical methods—the k -means discretizer, the χ 2 filter and a one-layer artificial neural network—have been reimplemented to be able to tackle online data, showing promising results on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the strategies for feature selection and multi-class classification in the emotion detection problem. The aim is two-fold: to increase the effectiveness of four feature selection algorithms and to improve accuracy of multi-class classifiers for emotion detection problem under different frameworks and strategies. Although, a large amount of research has been conducted to determine the most informative features in emotion detection, it is still an open problem to identify reliably discriminating features. As it is believed that highly informative features are more critical factor than classifier itself, recent studies have been focused on identifying the features that contribute more to the classification problem. In this paper, in order to improve the performance of multi-class SVMs in emotion detection, 58 features extracted from recorded speech samples are processed in two new frameworks to boost the feature selection algorithms. Evaluation of the final feature sets validates that the frameworks are able to select more informative subset of the features in terms of class-separability. Also it is found that among four feature selection algorithms, a recently proposed one, LSBOUND, significantly outperforms the others. The accuracy rate obtained in the proposed framework is the highest achievement reported so far in the literature for the same dataset. [Copyright &y& Elsevier]

Abstract: The increasing flow of digital information requires the extraction, filtering and classification of pertinent information from large volumes of texts. All these tasks greatly benefit from involving a Named Entity Recognizer (NER) in the preprocessing stage. This paper proposes a completely automatic NER system. The NER task involves not only the identification of proper names (Named Entities) in natural language text, but also their classification into a set of predefined categories, such as names of persons, organizations (companies, government organizations, committees, etc. ), locations (cities, countries, rivers, etc. ) and miscellaneous (movie titles, sport events, etc. ). Throughout the paper, we examine the differences between language models learned by different data-driven classifiers confronted with the same NLP task, as well as ways to exploit these differences to yield a higher accuracy than the best individual classifier. Three machine learning classifiers (Hidden Markov Model, Maximum Entropy and Memory Based Learning) are trained on the same corpus in order to resolve the NE task. After comparison, their output is combined using voting strategies. A comprehensive study and experimental work on the evaluation of our system, as well as a comparison with other systems has been carried out within the framework of two specialized scientific competitions for NER, CoNLL-2002 and HAREM-2005. Finally, this paper describes the integration of our NER system in different NLP applications, in concrete Geographic Information Retrieval and Conceptual Modelling. [Copyright &y& Elsevier]

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]

This paper proposes an ensemble algorithm named of BPSO-Adaboost-KNN to cope with multi-class imbalanced data classification. The main idea of this algorithm is to integrate feature selection and boosting into ensemble. What’s more, we utilize a novel evaluation metric called AUCarea which is especially for multi-class classification. In our model BPSO is employed as the feature selection algorithm in which AUCarea is chosen as the fitness. For classification, we generate a boosting classifier in which KNN is selected as the basic classifier. In order to verify the effectiveness of our method, 19 benchmarks are used in our experiments. The results show that the proposed algorithm improves both the stability and the accuracy of boosting after carrying out feature selection, and the performance of our algorithm is comparable with other state-of-the-art algorithms. In statistical analyses, we apply Bland–Altman analysis to show the consistencies between AUCarea and other popular metrics like average G-mean, average F -value etc. Besides, we use linear regression to find deeper correlation between AUCarea and other metrics in order to show why AUCarea works well in this issue. We also put out a series of statistical studies in order to analyze if there exist significant improvements after feature selection and boosting are employed. At last, the proposed algorithm is applied in oil-bearing of reservoir recognition. The classification precision is up to 99% in oilsk81-oilsk85 well logging data in Jianghan oilfield of China, which is 20% higher than KNN classifier. Particularly, the proposed algorithm has significant superiority when distinguishing the oil layer from other layers. [ABSTRACT FROM AUTHOR]

Classification reliability and accuracy are important components for any computer-aided diagnostic system. This paper presents a dermatology CADx system to automatically classify dermatology images into psoriatic lesion and healthy skin using an online system. The novelty of the system is an exploration of the unique and comprehensive feature space combined with classification in support vector machine (SVM) paradigm. The unique feature space consists of grayscale space, color space and aggressiveness of psoriatic disease such as redness and chaoticness. The proposed CADx framework is conventional in paradigm that it has offline and online components. The offline system trains using unique integrated feature space and apriori dermatologist derived ground truth. This training system yields machine learning parameters. The online system is applied on the incoming test images which get transformed by an online classifier utilizing the offline machine learning parameters. The accuracy of the system is evaluated using cross-validation procedure depending upon one of the three partition protocols such as (5-fold, 10-fold and Jack Knife). The proposed CADx system shows the classification accuracy of 99.53%, 99.66% and 99.81% for 5-fold, 10-fold and Jack Knife protocols respectively for 15 optimal features. Further, our results show that, we can demonstrate the reliability and consistency factor by showing the monotonously rising accuracy with increase in data size. Our system is benchmarked against previous reported systems and outstands besides being unique and novel. [ABSTRACT FROM AUTHOR]

This paper copes with the COGERSA waste collection process. Up to now, experts have been manually designed the process using a trial and error mechanism. This process is not globally optimized, since it has been progressively and locally built as council demands appear. Planning optimization algorithms usually solve it, but they need a fitness function to evaluate a route planning quality. The drawback is that even experts are not able to propose one in a straightforward way due to the complexity of the process. Hence, the goal of this paper is to build a fitness function though a preference framework, taking advantage of the available expert knowledge and expertise. Several key performance indicators together with preference judgments are carefully established according to the experts for learning a promising fitness function. Particularly, the additivity property of them makes the task be much more affordable, since it allows to work with routes rather than with route plannings. Besides, a feature selection analysis is performed over such indicators, since the experts suspect of a potential existing (but unknown) redundancy among them. The experiment results confirm this hypothesis, since the best C − index (98% against around 94%) is reached when 6 or 8 out of 21 indicators are taken. Particularly, truck load seems to be a highly promising key performance indicator, together to the travelled distance along non-main roads. A comparison with other existing approaches shows that the proposed method clearly outperforms them, since the C − index goes from 72% or 90% to 98%. • A fitness function to evaluate a waste collection route planning. • Key performance indicators for a waste collection route planning. • Feature selection of key performance indicators for a waste collection route planning. [ABSTRACT FROM AUTHOR]

This paper describes an approach for credit risk evaluation based on linear Support Vector Machines classifiers, combined with external evaluation and sliding window testing, with focus on application on larger datasets. It presents a technique for optimal linear SVM classifier selection based on particle swarm optimization technique, providing significant amount of focus on imbalanced learning issue. It is compared to other classifiers in terms of accuracy and identification of each class. Experimental classification performance results, obtained using real world financial dataset from SEC EDGAR database, lead to conclusion that proposed technique is capable to produce results, comparable to other classifiers, such as logistic regression and RBF network, and thus be can be an appealing option for future development of real credit risk evaluation models. [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 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]

Abstract: Feature selection is a combinatorial optimization problem that selects most relevant features from an original feature set to increase the performance of classification or clustering algorithms. Most feature selection methods are supervised methods and use the class labels as a guide. On the other hand, unsupervised feature selection is a more difficult problem due to the unavailability of class labels. In this paper, we present an unsupervised feature selection method based on ant colony optimization, called UFSACO. The method seeks to find the optimal feature subset through several iterations without using any learning algorithms. Moreover, the feature relevance will be computed based on the similarity between features, which leads to the minimization of the redundancy. Therefore, it can be classified as a filter-based multivariate method. The proposed method has a low computational complexity, thus it can be applied for high dimensional datasets. We compare the performance of UFSACO to 11 well-known univariate and multivariate feature selection methods using different classifiers (support vector machine, decision tree, and naïve Bayes). The experimental results on several frequently used datasets show the efficiency and effectiveness of the UFSACO method as well as improvements over previous related methods. [Copyright &y& Elsevier]

Abstract: The classification of power system operating states plays an important role in power system control and operation. Determining the state of a power system is crucial and requirements for real-time decision making in power system security assessment demand low dimensionality and low computational time. This paper investigates the benefits of using feature selection based on mutual information in power system state classification with machine learning. The AdaBoost algorithm is used for classification based on large training datasets and feature selection is applied in order to reduce their dimensionality. The selection is implemented as a filter in the pre-processing stage of AdaBoost and uses genetic algorithms to perform the search with the fitness function computed based on mutual information. The proposed method is tested on the IEEE New England 39-bus network and a comparison between the learning algorithm performances with and without feature selection is provided. Results for different genetic algorithm parameters are also presented. [Copyright &y& Elsevier]

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]

Abstract: Feature selection and classification techniques have been studied independently without considering the interaction between both procedures, which leads to a degraded performance. In this paper, we present a new neural network approach, which is called an algorithm learning based neural network (ALBNN), to improve classification accuracy by integrating feature selection and classification procedures. In general, a knowledge-based artificial neural network operates on prior knowledge from domain experience, which provides it with better starting points for the target function and leads to better classification accuracy. However, prior knowledge is usually difficult to identify. Instead of using unknown background resources, the proposed method utilizes prior knowledge that is mathematically calculated from the properties of other learning algorithms such as PCA, LARS, C4.5, and SVM. We employ the extreme learning machine in this study to help obtain better initial points faster and avoid irrelevant time-consuming work, such as determining architecture and manual tuning. ALBNN correctly approximates a target hypothesis by both considering the interaction between two procedures and minimizing individual procedure errors. The approach produces new relevant features and improves the classification accuracy. Experimental results exhibit improved performance in various classification problems. ALBNN can be applied to various fields requiring high classification accuracy. [Copyright &y& Elsevier]

Abstract: In this paper, we develop a diagnosis model based on particle swarm optimization (PSO), support vector machines (SVMs) and association rules (ARs) to diagnose erythemato-squamous diseases. The proposed model consists of two stages: first, AR is used to select the optimal feature subset from the original feature set; then a PSO based approach for parameter determination of SVM is developed to find the best parameters of kernel function (based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy, and PSO is a promising tool for global searching). Experimental results show that the proposed AR_PSO–SVM model achieves 98.91% classification accuracy using 24 features of the erythemato-squamous diseases dataset taken from UCI (University of California at Irvine) machine learning database. Therefore, we can conclude that our proposed method is very promising compared to the previously reported results. [Copyright &y& Elsevier]

Abstract: Many approaches are proposed to improve Naive Bayes, among which the attribute selection approach has demonstrated remarkable performance. Algorithms for attribute selection fall into two broad categories: filters and wrappers. Filters use the general data characteristics to evaluate the selected attribute subset before the learning algorithm is run, while wrappers use the learning algorithm itself as a black box to evaluate the selected attribute subset. In this paper, we work on the attribute selection approach of wrapper and propose an improved Naive Bayes algorithm by carrying a random search through the whole space of attributes. We simply called it Randomly Selected Naive Bayes (RSNB). In order to meet the need of classification, ranking, and class probability estimation, we discriminatively design three different versions: RSNB-ACC, RSNB-AUC, and RSNB-CLL. The experimental results based on a large number of UCI datasets validate their effectiveness in terms of classification accuracy (ACC), area under the ROC curve (AUC), and conditional log likelihood (CLL), respectively. [Copyright &y& Elsevier]

Abstract: Feature selection is one of the core issues in designing pattern recognition and machine learning systems, and has attracted considerable attention in the literature. In this paper, a new feature subset selection algorithm with conditional mutual information is proposed, which firstly guarantees to find a subset of which the mutual information with the class is the same as that of the original set of features, and then eliminates potential redundant features from the view of minimal information loss based on the cumulate conditional mutual information minimization criterion. From the reliability point of view, this criterion can also abate the disturbance caused by sample insufficiency in conditional mutual information estimation. In addition, a fast implementation of conditional mutual information estimation is proposed and used to tackle the computationally intractable problem. Empirical results verify that our algorithm is efficient and achieves better accuracy than several representative feature selection algorithms for three typical classifiers on various datasets. [Copyright &y& Elsevier]

Abstract: The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing, pattern classification, and data mining applications. This paper presents a novel optimization algorithm called catfish binary particle swarm optimization (CatfishBPSO), in which the so-called catfish effect is applied to improve the performance of binary particle swarm optimization (BPSO). This effect is the result of the introduction of new particles into the search space (“catfish particles”), which replace particles with the worst fitness by the initialized at extreme points of the search space when the fitness of the global best particle has not improved for a number of consecutive iterations. In this study, the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) was used to evaluate the quality of the solutions. CatfishBPSO was applied and compared to 10 classification problems taken from the literature. Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods. [Copyright &y& Elsevier]

Abstract: In this paper, a new wrapper method for feature selection, namely IAFN-FS (Incremental ANalysis Of VAriance and Functional Networks for Feature Selection) is presented. The method uses as induction algorithm the AFN (ANOVA and Functional Networks) learning method; follows a backward non-sequential strategy from the complete set of features (thus allowing to discard several variables in one step, and so reducing computational time); and is able to consider “multivariate” relations between features. An important characteristic of the method is that it permits the user the interpretation of the results obtained, because the relevance of each feature selected or rejected is given in terms of its variance. IAFN-FS is applied to several benchmark real-world classification data sets showing adequate performance results. Also, a comparison with the results obtained by other wrapper methods is carried out, showing that the proposed method obtains better performance results in average. [Copyright &y& Elsevier]

Abstract: In this paper, we developed a diagnosis model based on support vector machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our proposed hybrid feature selection method, named improved F-score and Sequential Forward Search (IFSFS), combines the advantages of filter and wrapper methods to select the optimal feature subset from the original feature set. In our IFSFS, we improved the original F-score from measuring the discrimination of two sets of real numbers to measuring the discrimination between more than two sets of real numbers. The improved F-score and Sequential Forward Search (SFS) are combined to find the optimal feature subset in the process of feature selection, where, the improved F-score is an evaluation criterion of filter method, and SFS is an evaluation system of wrapper method. The best parameters of kernel function of SVM are found out by grid search technique. Experiments have been conducted on different training-test partitions of the erythemato-squamous diseases dataset taken from UCI (University of California Irvine) machine learning database. Our experimental results show that the proposed SVM-based model with IFSFS achieves 98.61% classification accuracy and contains 21 features. With these results, we conclude our method is very promising compared to the previously reported results. [ABSTRACT FROM AUTHOR]

Abstract: The inclusion of irrelevant, redundant, and inconsistent features in the data-mining model results in poor predictions and high computational overhead. This paper proposes a novel information theoretic-based interact (IT-IN) algorithm, which concerns the relevance, redundancy, and consistency of the features. The proposed IT-IN algorithm is compared with existing Interact, FCBF, Relief and CFS feature selection algorithms. To evaluate the classification accuracy of IT-IN and remaining four feature selection algorithms, Naïve Bayes, SVM, and ELM classifier are used for ten UCI repository datasets. The proposed IT-IN performs better than existing above algorithms in terms of number of features. The specially designed hash function is used to speed up the IT-IN algorithms and provides minimum computation time than the Interact algorithms. The result clearly reveals that the proposed feature selection algorithm improves the classification accuracy for ELM, Naïve Bayes, and SVM classifiers. The performance of proposed IT-IN with ELM classifier is superior to other classifiers. [ABSTRACT FROM AUTHOR]

Abstract: There are irrelevant features that are redundant or significantly degrade the learning accuracy in the real-world complex classification tasks. This paper presents a new hybrid learning algorithm based on a cooperative coevolutionary algorithm (Co-CEA) with dual populations for designing the radial basis function neural network (RBFNN) models with an explicit feature selection. This approach attempts to complete both the RBFNN construction and the feature selection simultaneously. The proposed algorithm utilizes the Co-CEA’s divide-and-cooperative mechanism, which utilizes the evolutionary algorithms executing in parallel to coevolve subpopulations, corresponding to the hidden layer structure and the dominate features respectively. The algorithm adopts the binary encoding to represent the feature subset and the matrix-form mixed encoding to represent the RBFNN hidden layer structure, and a complete solution is formed via collaborations among the two subpopulations. Experimental results illustrate that the proposed algorithm outperforms other algorithms in references in terms of the classification accuracy, and it is able to obtain both prominent features and good RBFNN structure with higher prediction capability. [Copyright &y& Elsevier]

Abstract: Feature selection aims at determining a subset of available features which is most discriminative and informative for data analysis. This paper presents an effective feature selection method for support vector machine (SVM). Unlike the traditional combinatorial searching method, feature selection is translated into the model selection of SVM which has been well studied. In more detail, the basic idea of this method is to tune the hyperparameters of the Gaussian Automatic Relevance Determination (ARD) kernels via optimization of kernel polarization, and then to rank all features in decreasing order of importance so that more relevant features can be identified. We test the proposed method with some UCI machine learning benchmark examples and show that it can dramatically reduce the number of features and outperforms SVM trained using the features selected according to correlation coefficient and using all features. [Copyright &y& Elsevier]

Abstract: Algorithms for feature selection in predictive data mining for classification problems attempt to select those features that are relevant, and are not redundant for the classification task. A relevant feature is defined as one which is highly correlated with the target function. One problem with the definition of feature relevance is that there is no universally accepted definition of what it means for a feature to be ‘highly correlated with the target function or highly correlated with the other features’. A new feature selection algorithm which incorporates domain specific definitions of high, medium and low correlations is proposed in this paper. The proposed algorithm conducts a heuristic search for the most relevant features for the prediction task. [Copyright &y& Elsevier]

Abstract: In this paper, a new feature selection method based on Association Rules (AR) and Neural Network (NN) is presented for the diagnosis of erythemato-squamous diseases. AR is used for reducing the dimension of erythemato-squamous diseases dataset and NN is used for efficient classification. The proposed AR+NN system performance is compared with that of other feature selection algorithms+NN. The dimension of input feature space is reduced from thirty four to twenty four by using AR. In test stage, 3-fold cross validation method is applied to the erythemato-squamous diseases dataset to evaluate the proposed system performances. The correct classification rate of proposed system is 98.61%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR+NN model can be used to obtain fast automatic diagnostic systems for other diseases. [Copyright &y& Elsevier]

• 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]

• Proposed method jointly optimizes both feature selection and classification. • Proposed method can reduce extremely high numbers of features. • This study uses multiple LS algorithms, running in parallel and sharing information. Over the last decade, the number of studies on machine learning has significantly increased. One of the most widely researched areas of machine learning is data classification. Most big data systems require a large amount of information storage for analytic purposes; however, this involves some disadvantages, such as the costs of processing and collecting data. Thus, many researchers and practitioners are working on effectively reducing the number of features used in classification. This paper proposes a method which jointly optimizes both feature selection and classification. A survey of the relevant literature shows that the vast majority of studies focus on either feature selection or classification. In this study, the proposed parallel local search algorithm both selects features and finds a classifier with high rates of accuracy. Moreover, the proposed method is capable of finding solutions for problems that have extremely high numbers of features within a reasonable computation time. [ABSTRACT FROM AUTHOR]