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Feature Selection via Maximizing Neighborhood Soft Margin

  • Qinghua Hu
  • Xunjian Che
  • Jinfu Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5828)

Abstract

Feature selection is considered to be a key preprocessing step in machine learning and pattern recognition. Feature evaluation is one of the key issues for constructing a feature selection algorithm. In this work, we propose a new concept of neighborhood margin and neighborhood soft margin to measure the minimal distance between different classes. We use the criterion of neighborhood soft margin to evaluate the quality of candidate features and construct a forward greedy algorithm for feature selection. We conduct this technique on eight classification learning tasks. Compared with the raw data and other three feature selection algorithms, the proposed technique is effective in most of the cases.

Keywords

Feature Selection Feature Selection Algorithm Candidate Feature Feature Subset Selection Machine Learn Research 
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-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qinghua Hu
    • 1
  • Xunjian Che
    • 1
  • Jinfu Liu
    • 1
  1. 1.Harbin Institute of TechnlologyHarbinChina

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