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Feature Selection Based on Rough Set and Gravitational Search Algorithm

In: Proceedings of 20th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Hua-qiang Wang

    (Tianjin University)

  • Zhan-wen Niu

    (Tianjin University)

  • Li-jun Liang

    (Tianjin University)

Abstract

Many approaches have been tried out for feature selection, which is aimed at finding a minimal subset of the original features with predetermined targets. However, a complete search isn’t feasible for even medium-sized datasets and it has been proved that finding a minimal subset of the features is a NP-hard problem. Rough set theory is one of the effective methods to feature selection, and gravitational search algorithm (GSA), which has a flexible and well-balanced mechanism to enhance exploration and exploitation, has been successfully applied in many difficult problems. In this paper, a novel approach, called FSRG, for feature selection based on rough set and GSA is proposed, and 5 UCI datasets are used as an illustrated example. The results demonstrate that FSRG is an efficient method for feature selection.

Suggested Citation

  • Hua-qiang Wang & Zhan-wen Niu & Li-jun Liang, 2013. "Feature Selection Based on Rough Set and Gravitational Search Algorithm," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, edition 127, pages 409-418, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-40063-6_41
    DOI: 10.1007/978-3-642-40063-6_41
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