Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15225
Title: Adaptive weighted fusion of local kernel classifiers for effective pattern classification
Authors: Yang, S
Zuo, W
Liu, L
Li, Y
Zhang, D 
Keywords: Cassifier fusion
Kernel method
Local learning
Nearest neighbors
Support vector machine
Issue Date: 2011
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2011, v. 6838 LNCS, p. 63-70 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
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.
Description: 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, 11-14 August 2011
URI: http://hdl.handle.net/10397/15225
ISBN: 9783642247279
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-24728-6_9
Appears in Collections:Conference Paper

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