Adaptive weighted fusion of local kernel classifiers for effective pattern classification

Shixin Yang, Wangmeng Zuo, Lei Liu, Yanlai Li, Dapeng Zhang

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

4 Citations (Scopus)

Abstract

The theoretical and practical virtual of local learning algorithms had been verified by the machine learning community. The selection of the proper local classifier, however, remains a challenging problem. Rather than selecting one single local classifier, in this paper, we propose to choose several local classifiers and use adaptive fusion strategy to alleviate the choice problem of the proper local classifier. Based on the fast and scalable local kernel support vector machine (FaLK-SVM), we adopt the self-adaptive weighting fusion method for combining local support vector machine classifiers (FaLK-SVMa), and provide two fusion methods, distance-based weighting (FaLK-SVMad) and rank-based weighting methods (FaLK-SVMar). Experimental results on fourteen UCI datasets and three large scale datasets show that FaLK-SVMa can chieve higher classification accuracy than FaLK-SVM.
Original languageEnglish
Title of host publicationAdvanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers
Pages63-70
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2011
Event7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
Duration: 11 Aug 201114 Aug 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6838 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Intelligent Computing, ICIC 2011
Country/TerritoryChina
CityZhengzhou
Period11/08/1114/08/11

Keywords

  • classifier fusion
  • Kernel method
  • local learning
  • nearest neighbors
  • support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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