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An Adaption of Relief for Redundant Feature Elimination

  • Tianshu Wu
  • Kunqing Xie
  • Chengkai Nie
  • Guojie Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)

Abstract

Feature selection is important for many learning problems improving speed and quality. Main approaches include individual evaluation and subset evaluation methods. Individual evaluation methods, such as Relief, are efficient but can not detect redundant features, which limits the applications. A new feature selection algorithm removing both irrelevant and redundant features is proposed based on the basic idea of Relief. For each feature, not only effectiveness is evaluated, but also informativeness is considered according to the performance of other features. Experiments on bench mark datasets show that the new algorithm can removing both irrelevant and redundant features and keep the efficiency like a individual evaluation method.

Keywords

Feature selection Relief algorithm Redudant features 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tianshu Wu
    • 1
  • Kunqing Xie
    • 1
  • Chengkai Nie
    • 2
  • Guojie Song
    • 1
  1. 1.Key Laboratory of Machine Perception, Ministry of EducationPeking UniversityBeijingChina
  2. 2.Institute of Communications Planning Survey & Design of Shanxi ProvinceTaiyuanChina

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