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Immune Multiobjective Optimization Algorithm for Unsupervised Feature Selection

  • Xiangrong Zhang
  • Bin Lu
  • Shuiping Gou
  • Licheng Jiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

A feature selection method for unsupervised learning is proposed. Unsupervised feature selection is considered as a combination optimization problem to search for the suitable feature subset and the pertinent number of clusters by optimizing the efficient evaluation criterion for clustering and the number of features selected. Instead of combining these measures into one objective function, we make use of the multiobjective immune clonal algorithm with forgetting strategy to find the more discriminant features for clustering and the most pertinent number of clusters. The results of experiments on synthetic data and real datasets from UCI database show the effectiveness and potential of the method.

Keywords

Feature Selection Pareto Front Feature Subset Nondominated Solution Feature Subset Selection 
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 2006

Authors and Affiliations

  • Xiangrong Zhang
    • 1
  • Bin Lu
    • 1
  • Shuiping Gou
    • 1
  • Licheng Jiao
    • 1
  1. 1.National Key Lab for Radar Signal Processing, Institute of Intelligent Information ProcessingXidian UniversityXi’anChina

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