Adaptive weighted fusion of local kernel classifiers for effective pattern classification

Authors:
Shixin Yang;Wangmeng Zuo;Lei Liu;Yanlai Li;David Zhang
Affiliations:
Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

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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.