Adaptive Weighted Fusion of Local Kernel Classifiers for Effective Pattern Classification

  • Shixin Yang
  • Wangmeng Zuo
  • Lei Liu
  • Yanlai Li
  • David Zhang
Conference paper

DOI: 10.1007/978-3-642-24728-6_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6838)
Cite this paper as:
Yang S., Zuo W., Liu L., Li Y., Zhang D. (2011) Adaptive Weighted Fusion of Local Kernel Classifiers for Effective Pattern Classification. In: Huang DS., Gan Y., Bevilacqua V., Figueroa J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg

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 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shixin Yang
    • 1
  • Wangmeng Zuo
    • 1
  • Lei Liu
    • 1
  • Yanlai Li
    • 1
  • David Zhang
    • 1
    • 2
  1. 1.Biocomputing Research Centre, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of ComputingThe Hong Kong Polytechnic UniversityKowloonHong Kong

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