A boundary based classifier combination method

Authors:
Ming Liu;Kunlun Li;Rui Zhao
Affiliations:
College of Electronic and Information Engineering, Hebei University, Baoding, China;College of Electronic and Information Engineering, Hebei University, Baoding, China;College of Electronic and Information Engineering, Hebei University, Baoding, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

In this paper, a new classifier combination method is proposed for two-class problems. The boundaries of the classes are extracted directly from the given training set, and a set of linear combination rules are defined based on each sample on the class boundaries. The new approach is tested on two large public datasets, and the experimental results show its good performances. Comparing with combination methods such as linear combination, voting, decision templates, our method has higher classification accuracy; comparing with the k-NN rule, its computational complexity is much lower.