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

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

Pooja Rani, Rajneesh Kumar, Anurag Jain, Sunil Kumar Chawla
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 22
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781799861515|DOI: 10.4018/IJISMD.2021040102
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MLA

Rani, Pooja, et al. "A Hybrid Approach for Feature Selection Based on Genetic Algorithm and Recursive Feature Elimination." IJISMD vol.12, no.2 2021: pp.17-38. http://doi.org/10.4018/IJISMD.2021040102

APA

Rani, P., Kumar, R., Jain, A., & Chawla, S. K. (2021). A Hybrid Approach for Feature Selection Based on Genetic Algorithm and Recursive Feature Elimination. International Journal of Information System Modeling and Design (IJISMD), 12(2), 17-38. http://doi.org/10.4018/IJISMD.2021040102

Chicago

Rani, Pooja, et al. "A Hybrid Approach for Feature Selection Based on Genetic Algorithm and Recursive Feature Elimination," International Journal of Information System Modeling and Design (IJISMD) 12, no.2: 17-38. http://doi.org/10.4018/IJISMD.2021040102

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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.

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