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A hybrid genetic algorithm for feature subset selection in rough set theory

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Abstract

Rough set theory has been proven to be an effective tool to feature subset selection. Current research usually employ hill-climbing as search strategy to select feature subset. However, they are inadequate to find the optimal feature subset since no heuristic can guarantee optimality. Due to this, many researchers study stochastic methods. Since previous works of combination of genetic algorithm and rough set theory do not show competitive performance compared with some other stochastic methods, we propose a hybrid genetic algorithm for feature subset selection in this paper, called HGARSTAR. Different from previous works, HGARSTAR embeds a novel local search operation based on rough set theory to fine-tune the search. This aims to enhance GA’s intensification ability. Moreover, all candidates (i.e. feature subsets) generated in evolutionary process are enforced to include core features to accelerate convergence. To verify the proposed algorithm, experiments are performed on some standard UCI datasets. Experimental results demonstrate the efficiency of our algorithm.

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Authors and Affiliations

  1. School of Computer Science, Leshan Normal University, Leshan, 614000, China

    Si-Yuan Jing

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  1. Si-Yuan Jing
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Correspondence to Si-Yuan Jing.

Additional information

Communicated by V. Loia.

Appendix: Evolution process on different datasets

Appendix: Evolution process on different datasets

See Figs. 2, 3, 4, 5, 6 and 7.

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Evolution process on lymphography

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Evolution process on soybean-small

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Fig. 7
figure 7

Evolution process on lung-cancer

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Jing, SY. A hybrid genetic algorithm for feature subset selection in rough set theory. Soft Comput 18, 1373–1382 (2014). https://doi.org/10.1007/s00500-013-1150-3

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  • DOI: https://doi.org/10.1007/s00500-013-1150-3

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