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A Hybrid Approach for Feature Selection Based on Genetic Algorithm and Recursive Feature Elimination

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  • Pooja Rani

    (Maharishi Markandeshwar Engineering College, MM Institute of Computer Technology and Business Management, Maharishi Markandeshwar (Deemed), Mullana, India)

  • Rajneesh Kumar

    (Department of CSE, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed), Mullana, India)

  • Anurag Jain

    (Virtualization Department, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India)

  • Sunil Kumar Chawla

    (Computer Science and Engineering, University Institute of Engineering, Chandigarh University, Punjab, India)

Abstract

Machine learning has become an integral part of our life in today's world. Machine learning when applied to real-world applications suffers from the problem of high dimensional data. Data can have unnecessary and redundant features. These unnecessary features affect the performance of classification systems used in prediction. Selection of important features is the first step in developing any decision support system. In this paper, the authors have proposed a hybrid feature selection method GARFE by integrating GA (genetic algorithm) and RFE (recursive feature elimination) algorithms. Efficiency of proposed method is analyzed using support vector machine classifier on the scale of accuracy, sensitivity, specificity, precision, F-measure, and execution time parameters. Proposed GARFE method is also compared to eight other feature selection methods. Results demonstrate that the proposed GARFE method has increased the performance of classification systems by removing irrelevant and redundant features.

Suggested Citation

  • Pooja Rani & Rajneesh Kumar & Anurag Jain & Sunil Kumar Chawla, 2021. "A Hybrid Approach for Feature Selection Based on Genetic Algorithm and Recursive Feature Elimination," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 12(2), pages 17-38, April.
    • Handle: RePEc:igg:jismd0:v:12:y:2021:i:2:p:17-38
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