Chapter

Advanced Intelligent Computing

Volume 6838 of the series Lecture Notes in Computer Science pp 63-70

Adaptive Weighted Fusion of Local Kernel Classifiers for Effective Pattern Classification

  • Shixin YangAffiliated withLancaster UniversityBiocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology
  • , Wangmeng ZuoAffiliated withLancaster UniversityBiocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology
  • , Lei LiuAffiliated withLancaster UniversityBiocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology
  • , Yanlai LiAffiliated withLancaster UniversityBiocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology
  • , David ZhangAffiliated withLancaster UniversityBiocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of TechnologyDepartment of Computing, The Hong Kong Polytechnic University

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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.

Keywords

Kernel method support vector machine local learning classifier fusion nearest neighbors