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Combining Evolutionary and Sequential Search Strategies for Unsupervised Feature Selection

  • Artur Klepaczko
  • Andrzej Materka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

Abstract

The research presented in this paper aimed at development of a robust feature space exploration technique for unsupervised selection of its subspace for feature vectors classification. Experiments with synthetic and textured image data sets show that current sequential and evolutionary strategies are inefficient in the cases of large feature vector dimensions (reaching the order of 102) and multiple-class problems. Thus, the proposed approach utilizes the concept of hybrid genetic algorithm and adopts it for specific requirements of unsupervised learning.

Keywords

Unsupervised feature selection hybrid genetic algorithm texture analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Artur Klepaczko
    • 1
  • Andrzej Materka
    • 1
  1. 1.Institute of ElectronicsTechnical University of LodzLodz

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