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Attribute Reduction Based on Rough Sets and the Discrete Firefly Algorithm

  • Nguyen Cong Long
  • Phayung Meesad
  • Herwig Unger
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)

Abstract

Attribute reduction is used to allow elimination of redundant attributes while remaining full meaning of the original dataset. Rough sets have been used as attribute reduction techniques with much success. However, rough set applies to attribute reduction are inadequate at finding optimal reductions. This paper proposes an optimal attribute reduction strategy relying on rough sets and discrete firefly algorithm. To demonstrate the applicability and superiority of the proposed model, comparison between the proposed models with existing well-known methods is also investigated. The experiment results illustrate that performances of the proposed model when compared to other attribute reduction can provide comparative solutions efficiently.

Keywords

Attribute Reduction Reduction Feature selection Rough sets Core Firefly Algorithm 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nguyen Cong Long
    • 1
  • Phayung Meesad
    • 1
  • Herwig Unger
    • 2
  1. 1.Faculty of Information TechnologyKing Mongkut’s University of TechnologyNorth BangkokThailand
  2. 2.Faculty of Mathematics and Computer ScienceFernUniversitat in HagenHagenGermany

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