Feature Weighting Algorithm Based on Margin and Linear Programming

  • Wei Pan
  • Peijun Ma
  • Xiaohong Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7413)

Abstract

Feature selection is an important task in machine learning. In this work, we design a robust algorithm for optimal feature subset selection. We present a global optimization technique for feature weighting. Margin induced loss functions are introduced to evaluate features, and we employs linear programming to search the optimal solution. The derived weights are combined with the nearest neighbor rule. The proposed technique is tested on UCI data sets. Compared with Simba and LMFW, the proposed technique is effective and efficient.

Keywords

Feature Selection Loss Function Class Label Feature Selection Method Feature Selection Algorithm 
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 2012

Authors and Affiliations

  • Wei Pan
    • 1
  • Peijun Ma
    • 1
  • Xiaohong Su
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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