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Binarization of the Swallow Swarm Optimization for Feature Selection

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Abstract

In this paper, we propose six methods for binarization of the swallow swarm optimization (SSO) algorithm to solve the feature selection problem. The relevance of the selected feature subsets is estimated by two classifiers: a fuzzy rule-based classifier and a classifier based on k-nearest neighbors. To find an optimal subset of features, we take into account the number of features and classification accuracy. The developed algorithms are tested on datasets from the KEEL repository. For the statistical evaluation of the binarization methods, we use Friedman’s two-way analysis of variance by ranks for related samples. The best feature selection result is shown by a hybrid method based on modified algebraic operations and MERGE operation introduced by the authors of this paper. The best classification accuracy is achieved with a V-shaped transfer function.

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Funding

This work was supported by the Ministry of Science and Higher Education of the Russian Federation, project no. FEWM-2020-0042.

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Authors and Affiliations

  1. Tomsk State University of Control Systems and Radioelectronics, pr. Lenina 40, 634050, Tomsk, Russia

    A. O. Slezkin, I. A. Hodashinsky & A. A. Shelupanov

Authors
  1. A. O. Slezkin
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  2. I. A. Hodashinsky
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  3. A. A. Shelupanov
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Corresponding authors

Correspondence to A. O. Slezkin, I. A. Hodashinsky or A. A. Shelupanov.

Additional information

Translated by Yu. Kornienko

APPENDIX

APPENDIX

Table A1. Accuracy values for the fuzzy classifier
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Table A2. Number of selected features for the fuzzy classifier
Full size table
Table A3. Accuracy values for the 5NN classifier
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Table A4. Number of selected features for the 5NN classifier
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Slezkin, A.O., Hodashinsky, I.A. & Shelupanov, A.A. Binarization of the Swallow Swarm Optimization for Feature Selection. Program Comput Soft 47, 374–388 (2021). https://doi.org/10.1134/S0361768821050066

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  • DOI: https://doi.org/10.1134/S0361768821050066

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