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Similarity Measurement and Feature Selection Using Genetic Algorithm

  • Shangfei Wang
  • Shan He
  • Hua Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)

Abstract

This paper proposes a novel approach to search for the optimal combination of a measure function and feature weights using an evolutionary algorithm. Different combinations of measure function and feature weights are used to construct the searching space. Genetic Algorithm is applied as an evolutionary algorithm to search for the candidate solution, in which the classification rate of the K-Nearest Neighbor classifier is used as the fitness value. Three experiments are carefully designed to show the attractiveness of our approach. In the first experiment, an artificial data set is constructed to verify the effectiveness of the proposed approach by testing whether it could find the optimal combination of measure function and feature weights which satisfy the data set. In the second experiment, data sets from the University of California at Irvine are employed to verify the general applicability of the method. Finally, a prostate cancer data set is used to show its effectiveness on high-dimensional data.

Keywords

feature selection measure function genetic algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shangfei Wang
    • 1
  • Shan He
    • 1
  • Hua Zhu
    • 1
  1. 1.Key Lab of Computing and Communicating Software of Anhui Province, School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiP.R. China

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