Parsimonious feature extraction based on genetic algorithms and support vector machines

Qijun Zhao, Hongtao Lu, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Most existing feature extraction algorithms aim at best preserving information in the original data or at improving the separability of data, but fail to consider the possibility of further reducing the number of used features. In this paper, we propose a parsimonious feature extraction algorithm. Its motivation is using as few features as possible to achieve the same or even better classification performance. It searches for the optimal features using a genetic algorithm and evaluates the features referring to Support Vector Machines. We tested the proposed algorithm by face recognition on the Yale and FERET databases. The experimental results proved its effectiveness and demonstrated that parsimoniousness should be a significant factor in developing efficient feature extraction algorithms.
Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2006
Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Proceedings
PublisherSpringer Verlag
Number of pages7
ISBN (Print)354034439X, 9783540344391
Publication statusPublished - 1 Jan 2006
Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
Duration: 28 May 20061 Jun 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3971 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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