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	Generating fuzzy rules for classifier fusion
	Liu, M ; Yuan, BZ ; Tang, XF ; Li, M
	2005
关键词	classification multiple classifier fusion fuzzy system FRAMEWORK
英文摘要	This paper addresses the issues in developing a fuzzy system for multiple classifier fusion. The proposed new approach has advantage in automatically generating fuzzy rules and determining the structure of the fuzzy system. Partitioning the attribute space into small regions can increase the description accuracy of the fuzzy system, but the number of training samples fall in each region will be decreased, and the reliability of the derived rules will be decreased also. To resolve this problem, we use membership functions with broad support set in the learning step, and propose an evaluation method to determine the structure of the fuzzy system. Experimental results are compared with other classifier fusion methods such as multi-response linear regression and neural network.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000233670801029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; CPCI-S(ISTP); 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/387239]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Liu, M,Yuan, BZ,Tang, XF,et al. Generating fuzzy rules for classifier fusion. 2005-01-01.

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