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	Satisfactory feature selection and its application enterprise credit assessment
	Ling, Jian ; Lin, Chengde ; Lin CD(林成德)
	2007
关键词	enterprise credit assessmen feature selection satisfactory optimization support vector machine (SVM)
英文摘要	The selection of evaluating index system is one of the key problems in enterprise credit assessment. It is essentially a satisfactory feature selection (SFS) problem. In this paper, several novel satisfactory-rate functions of feature set (SRFFS) are designed, in which the classification performance of the feature subset and its size are considered compromisingly. The accuracy of SVM Cross Validation is employed as evaluation criterion of classification ability, and the SFS algorithm is described in detail. Contrastive experiments are carried on SFS and three other different feature selection methods: S-SFS, Expert+GAFS and GAFS. Results show that SFS, which can pick out the feature subset with low dimension, high classification accuracy and balanced ranking performance, is superior to three other ones.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/70953]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Ling, Jian,Lin, Chengde,Lin CD. Satisfactory feature selection and its application enterprise credit assessment[J],2007.
APA	Ling, Jian,Lin, Chengde,&林成德.(2007).Satisfactory feature selection and its application enterprise credit assessment..
MLA	Ling, Jian,et al."Satisfactory feature selection and its application enterprise credit assessment".(2007).

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