Feature selection via Least Squares Support Feature Machine
Li, JP; Chen, ZY; Wei, LW; Xu, WX; Kou, G
2007
Source PublicationINTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
ISSN0219-6220
Volume6Issue:4Pages:16,671-686
AbstractIn many applications such as credit risk management, data are represented as high-dimensional feature vectors. It makes the feature selection necessary to reduce the computational complexity, improve the generalization ability and the interpretability. In this paper, we present a novel feature selection method -"Least Squares Support Feature Machine" (LS-SFM). The proposed method has two advantages comparing with conventional Support Vector Machine (SVM) and LS-SVM. First, the convex combinations of basic kernels are used as the kernel and each basic kernel makes use of a single feature. It transforms the feature selection problem that cannot be solved in the context of SVM to an ordinary multiple-parameter learning problem. Second, all parameters are learned by a two stage iterative algorithm. A 1-norm based regularized cost function is used to enforce sparseness of the feature parameters. The " support features" refer to the respective features with nonzero feature parameters. Experimental study on some of the UCI datasets and a commercial credit card dataset demonstrates the effectiveness and efficiency of the proposed approach.
KeywordFeature Selection Support Vector Machine Credit Assessment
Subject AreaComputer Science ; Artificial Intelligence ; Information Systems ; Interdisciplinary Applications ; Computer Science ; Computer Science ; Operations Research & Management Science
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.casisd.cn/handle/190111/4795
Collection中国科学院科技政策与管理科学研究所(1985年6月-2015年12月)
Recommended Citation
GB/T 7714
Li, JP,Chen, ZY,Wei, LW,et al. Feature selection via Least Squares Support Feature Machine[J]. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING,2007,6(4):16,671-686.
APA Li, JP,Chen, ZY,Wei, LW,Xu, WX,&Kou, G.(2007).Feature selection via Least Squares Support Feature Machine.INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING,6(4),16,671-686.
MLA Li, JP,et al."Feature selection via Least Squares Support Feature Machine".INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 6.4(2007):16,671-686.
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