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	An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
	Abedin, Mohammad Zoynul; Guotai, Chi; Fahmida-E-Moula; Zhang, Tong; Hassan, M. Kabir
刊名	JOURNAL OF RISK MODEL VALIDATION
	2019
卷号	13页码:1-46
关键词	data mining filter approach embedded methods training/testing instances intelligent algorithms credit risk evaluation
ISSN号	1753-9579
URL标识	查看原文
WOS记录号	[DB:DC_IDENTIFIER_WOSID]
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/3231642
专题	大连理工大学
作者单位	1.Dalian Maritime Univ, Sch Maritime Econ & Management, Collaborat Innovat Ctr Transport Studies, Dalian 116026, Peoples R China.,Hajee Mohammad Danesh Sci & Technol Univ, Dept Finance & Banking, Dinajpur 5200, Bangladesh. 2.Dalian Univ Technol, Fac Management & Econ, Dalian 116024, Peoples R China. 3.Univ New Orleans, Dept Econ & Finance, New Orleans, LA 70148 USA.
推荐引用方式 GB/T 7714	Abedin, Mohammad Zoynul,Guotai, Chi,Fahmida-E-Moula,et al. An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data[J]. JOURNAL OF RISK MODEL VALIDATION,2019,13:1-46.
APA	Abedin, Mohammad Zoynul,Guotai, Chi,Fahmida-E-Moula,Zhang, Tong,&Hassan, M. Kabir.(2019).An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data.JOURNAL OF RISK MODEL VALIDATION,13,1-46.
MLA	Abedin, Mohammad Zoynul,et al."An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data".JOURNAL OF RISK MODEL VALIDATION 13(2019):1-46.

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