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	A self region based real-valued negative selection algorithm
	Zhang FB(张凤斌) ; Wang DW(王大伟) ; Wang SW(王胜文) ; ZHANG Feng-bin ; WANG Da-wei ; WANG Sheng-wen
	2010-05-12 ; 2010-05-12
关键词	artificial immune real-valued negative selection cluster analysis self region partial training TP301.6
其他题名	A self region based real-valued negative selection algorithm
中文摘要	Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.; Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.
语种	英语 ; 英语
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/28314]
专题	清华大学
推荐引用方式 GB/T 7714	Zhang FB,Wang DW,Wang SW,et al. A self region based real-valued negative selection algorithm[J],2010, 2010.
APA	张凤斌,王大伟,王胜文,ZHANG Feng-bin,WANG Da-wei,&WANG Sheng-wen.(2010).A self region based real-valued negative selection algorithm..
MLA	张凤斌,et al."A self region based real-valued negative selection algorithm".(2010).

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