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Information and Rough Set Theory Based Feature Selection Techniques

  • Liam Cervante
  • Xiaoying Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)

Abstract

Feature selection is a well known and studied technique that aims to solve “the curse of dimensionality” and improve performance by removing irrelevant and redundant features. This paper highlights some well known approaches to filter feature selection, information theory and rough set theory, and compares a recent fitness function with some traditional methods. The contributions of this paper are two-fold. First, new results confirm previous research and show that the recent fitness function can also perform favorably when compared to rough set theory. Secondly, the measure of redundancy that is used in traditional information theory is shown to damage the performance when a similar approach is applied to the recent fitness function.

Keywords

Feature Selection Mutual Information Feature Subset Feature Selection Algorithm Joint Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Liam Cervante
    • 1
  • Xiaoying Gao
    • 1
  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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