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	Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases
	Xie,Juanying1; Lei,Jinhu1; Xie,Weixin2; Shi,Yong3; Liu,Xiaohui4
刊名	Health information science and systems
	2013-05-30
卷号	1期号:1
ISSN号	2047-2501
DOI	10.1186/2047-2501-1-10
通讯作者	Xie,juanying(xiejuany@snnu.edu.cn)
英文摘要	Abstractabstractthis paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. the two-stage hybrid algorithms adopt support vector machines (svm) as a classification tool, and the extended sequential forward search (sfs), sequential forward floating search (sffs), and sequential backward floating search (sbfs), respectively, as search strategies, and the generalized f-score (gf) to evaluate the importance of each feature. the new accuracy measure is used as the criterion to evaluated the performance of a temporary svm to direct the feature selection algorithms. these hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. to get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. we repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases.
语种	英语
出版者	BioMed Central
WOS记录号	BMC:10.1186/2047-2501-1-10
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2374333
专题	中国科学院大学
通讯作者	Xie,Juanying
作者单位	1.Shaanxi Normal University; School of computer science 2.Shenzhen University; School of Information Engineering 3.Chinese Academy of Sciences; CAS Research Centre of Fictitious Economy & Data Science 4.Brunel University; School of Information systems, Computing and Mathematics
推荐引用方式 GB/T 7714	Xie,Juanying,Lei,Jinhu,Xie,Weixin,et al. Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases[J]. Health information science and systems,2013,1(1).
APA	Xie,Juanying,Lei,Jinhu,Xie,Weixin,Shi,Yong,&Liu,Xiaohui.(2013).Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases.Health information science and systems,1(1).
MLA	Xie,Juanying,et al."Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases".Health information science and systems 1.1(2013).

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