Journal
IEEE ACCESS
Volume 9, Issue -, Pages 26766-26791Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3056407
Keywords
Feature extraction; Search problems; Biomedical imaging; Task analysis; Particle swarm optimization; Measurement; Machine learning algorithms; Binary variants; classification; feature selection; literature review; metaheuristic algorithms
Categories
Funding
- Ministry of Human Resource Development (MHRD Govt.)
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Feature selection is a critical task in machine learning, with metaheuristic algorithms attracting great attention in solving this problem. This paper reviews literature from 2009 to 2019 on solving feature selection problem using metaheuristic algorithms, highlighting challenges and issues, and categorizing over a hundred metaheuristic algorithms.
Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.
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