Abstract
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.
Highlights
The real-world problems mostly include a large number of data, and handling the data becomes a very complex and prominent task
Exhaustive search, greedy search, random search etc. are such techniques which have been applied to feature selection problems to find the best subset
The main contribution of presenting this study is given as: (a) This paper presents the definitions and techniques of feature selection problem, and basic concepts of metaheuristic algorithms are thoroughly explained. (b) The metaheuristic algorithms are classified, and a list of metaheuristic algorithms is given. (c) It presents an extensive literature of binary metaheuristic algorithms for feature selection problem. (d) The literature is represented with the vital factor of wrapper feature selection techniques such as the description of the classifier, name of the used datasets, evaluation metrics etc. (e) It explains the issues and challenges to develop an algorithm in solving feature selection problems
Summary
The real-world problems mostly include a large number of data, and handling the data becomes a very complex and prominent task. This study provides an extensive literature survey on metaheuristic algorithms which are developed in the last ten years (2009-2019) and applied to various applications of feature selection problems. Sharma and Kaur [4] presented a systematic review on nature-inspired algorithms to feature selection problem, especially in medical datasets. (c) It presents an extensive literature of binary metaheuristic algorithms for feature selection problem. (e) It explains the issues and challenges to develop an algorithm in solving feature selection problems It presents the evaluation metrics formula to investigate the performance.
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