A genetic feature weighting scheme for pattern recognition

Heesung Lee, Euntai Kim, Mignon Park

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

This paper proposes a new pattern recognition scheme, combining a new adaptive feature weighting and modified k-Nearest Neighbor (k-NN) rule. The proposed feature weighting method named adaptive-3FW. It uses three non-uniform weight levels (zero weight, middle weight and full weight) to weight each feature. The middle weight value is determined using genetic algorithms (GAs). The proposed adaptive-3FW overcomes overfitting issues and achieves high recognition performance. Novel GA operators tailored for this formulation are introduced to implement the proposed scheme. Further, a modified k-NN is proposed which uses a class-dependent feature weighting strategy. Whilst the conventional pattern recognition systems use the same set of feature weights for all classes, the proposed algorithm uses different sets of feature weights for different classes. Experiments were performed with the UCI repository for machine learning databases and the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.

Original languageEnglish
Pages (from-to)161-171
Number of pages11
JournalIntegrated Computer-Aided Engineering
Volume14
Issue number2
Publication statusPublished - 2007 Aug 7

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All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Artificial Intelligence

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