A class-specific metaheuristic technique for explainable relevant feature selection

Ezenkwu, Chinedu Pascal; Akpan, Uduak Idio; Stephen, Bliss Utibe-Abasi

doi:10.1016/j.mlwa.2021.100142

Cited by 8 publications

(8 citation statements)

References 30 publications

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“…The images were preprocessed and extracted features from the region to train the modified SVM and KNN classifier. Moreover, Ezenkwu et al [ 69 ] combined the SVM and random forest classifiers, and Sesmero et al [ 216 ] formalized and evaluated an ensemble of classifiers designed to resolve multiclass problems.…”

Section: Issues and Challengesmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

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Section: Issues and Challengesmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Traditional feature selection methods choose a single subset of the features as the "optimal" subset for the entire dataset. Apart from the commonly used global approach, some studies [8,6,9,10,11,12,13,14] have used class-specific approaches for selecting features, where for each class, a unique subset of the original features is selected. If there are C classes, in the class-specific approach, C subsets are chosen.…”

Section: Introductionmentioning

confidence: 99%

“…Different sets of features could be redundant for different classes. The class-specific feature selection (CSFS) works in [8,6,9,10,11,12,13,14] have proposed suitable frameworks that exploit classspecific feature subsets to solve classification problems. They have shown that the classifiers built with the subsets chosen by class-specific methods performed better than or comparable to the classifiers built with subsets chosen by the traditional global feature selection methods.…”

Section: Introductionmentioning

confidence: 99%

“…They have shown that the classifiers built with the subsets chosen by class-specific methods performed better than or comparable to the classifiers built with subsets chosen by the traditional global feature selection methods. The CSFS may also enhance the transparency/explainability of the classification process associated with it [6].…”

Section: Introductionmentioning

confidence: 99%

“…The majority of the CSFS methods [8,6,10,11,13,14] follow the one-versusall (OVA) strategy to decompose a C-class classification problem into C binary classification problems. They choose C class-specific feature subsets optimal for the C binary classification problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding the classes better with class-specific and rule-specific feature selection, and redundancy control in a fuzzy rule based framework

Das¹,

Pal²

2022

Preprint

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Recently, several studies have claimed that using class-specific feature subsets provides certain advantages over using a single feature subset for representing the data for a classification problem. Unlike traditional feature selection methods, the class-specific feature selection methods select an optimal feature subset for each class. Typically class-specific feature selection (CSFS) methods use one-versus-all split of the data set that leads to issues such as class imbalance, decision aggregation, and high computational overhead. We propose a class-specific feature selection method embedded in a fuzzy rule-based classifier, which is free from the drawbacks associated with most existing class-specific methods. Additionally, our method can be adapted to control the level of redundancy in the class-specific feature subsets by adding a suitable regularizer to the learning objective. Our method results in class-specific rules involving class-specific subsets. We also propose an extension where different rules of a particular class are defined by different feature subsets to model different substructures within the class. The effectiveness of the proposed method has been validated through experiments on three synthetic data sets.

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A novel feature selection method using generalized inverted Dirichlet-based HMMs for image categorization

Nasfi

Bouguila

2022

Int. J. Mach. Learn. & Cyber.

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A class-specific metaheuristic technique for explainable relevant feature selection

Cited by 8 publications

References 30 publications

Multiclass feature selection with metaheuristic optimization algorithms: a review

Multiclass feature selection with metaheuristic optimization algorithms: a review

Understanding the classes better with class-specific and rule-specific feature selection, and redundancy control in a fuzzy rule based framework

A novel feature selection method using generalized inverted Dirichlet-based HMMs for image categorization

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