Bagging evolutionary feature extraction algorithm for classification

Tianwen Zhao, Hongtao Lu, Qijun Zhao, Dapeng Zhang

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

5 Citations (Scopus)


Feature extraction is significant for pattern analysis and classification. Those based on genetic algorithms are promising owing to their potential parallelizability and possible applications in large scale and high dimensional data classification. Most recently, Zhao et al. presented a direct evolutionary feature extraction algorithm(DEFE) which can reduce the space complexity and improve the efficiency, thus overcoming the limitations of many genetic algorithm based feature extraction algorithms(EFE). However, DEFE does not consider the outlier problem which could deteriorate the classification performance, especially when the training sample set is small. Moreover, when there are many classes, the null space of within-class scatter matrix(Sw) becomes small, resulting in poor discrimination performance in that space. In this paper, we propose a bagging evolutionary feature extraction algorithm(BEFE) incorporating bagging into a revised DEFE algorithm to improve the DEFE's performance in cases of small training sets and large number of classes. The proposed algorithm has been applied to face recognition and testified using the Yale and ORL face databases.
Original languageEnglish
Title of host publicationProceedings - Third International Conference on Natural Computation, ICNC 2007
Number of pages6
Publication statusPublished - 1 Dec 2007
Event3rd International Conference on Natural Computation, ICNC 2007 - Haikou, Hainan, China
Duration: 24 Aug 200727 Aug 2007


Conference3rd International Conference on Natural Computation, ICNC 2007
CityHaikou, Hainan

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics
  • Modelling and Simulation


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