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A Concise Survey on Solving Feature Selection Problems with Metaheuristic Algorithms

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Advances in Electrical and Computer Technologies (ICAECT 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 881))

Abstract

In machine learning, feature selection is a crucial and important operation. The basic goal of the feature selection problem is to lower the size of the feature set while preserving performance accuracy. To classify the datasets, many approaches have been devised. Metaheuristic algorithms, on the other hand, have received a lot of interest for handling a variety of optimization problems. As a result, this paper gives a comprehensive assessment of the literature on employing metaheuristic algorithms to solve feature selection problems. Furthermore, based on their behavior, metaheuristic algorithms have been divided into four types. A classified list of over a hundred metaheuristic algorithms is also provided. Only binary variations of metaheuristic algorithms have been evaluated and categorized in order to address the feature selection problem, and a comprehensive explanation of each has been provided. The binary classification, name of the classifier used, datasets, and evaluation metrics for metaheuristic algorithms used to solve feature selection problems are all included. After analyzing the articles, problems and issues in getting the optimal feature subset using various metaheuristic methods are identified. Finally, several research needs are identified for researchers who want to continue their work on building or refining metaheuristic categorization algorithms. A case study is presented for an application in which UCI datasets are adopted and a number of metaheuristic algorithms are used to achieve the optimum functionality.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

    Rama Krishna Eluri & Nagaraju Devarakonda

Authors
  1. Rama Krishna Eluri
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  2. Nagaraju Devarakonda
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Correspondence to Rama Krishna Eluri .

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Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, SVS College of Engineering, Coimbatore, India

    Thangaprakash Sengodan

  2. Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Safat, Kuwait

    M. Murugappan

  3. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

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Eluri, R.K., Devarakonda, N. (2022). A Concise Survey on Solving Feature Selection Problems with Metaheuristic Algorithms. In: Sengodan, T., Murugappan, M., Misra, S. (eds) Advances in Electrical and Computer Technologies. ICAECT 2021. Lecture Notes in Electrical Engineering, vol 881. Springer, Singapore. https://doi.org/10.1007/978-981-19-1111-8_18

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  • DOI: https://doi.org/10.1007/978-981-19-1111-8_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1110-1

  • Online ISBN: 978-981-19-1111-8

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