NDLI: Hypothesis-margin Model Incorporating Structure Information for Feature Selection

Content Provider	IEEE Xplore Digital Library
Author	Ming Yang Ping Yang
Copyright Year	2009
Abstract	Iterative search margin based algorithm (Simba) has been proven effective for feature selection. However, the previously proposed model does not effectively utilize the structure information hidden in data which may have a great impact on the generalization performance of post-analysis classifiers. In this paper, we introduce a novel hypothesis-margin model incorporating structure information for feature selection(Ssimba_FS). In the newly developed model, the structure information induced by clustering algorithms is incorporated into the existing hypothesis margin model for feature selection, and meanwhile the contribution of the structure information can be effectively adjusted by a trade-off parameter. Based on Ssimba_FS, we present a novel algorithm for feature selection(Ssimba). By Ssimba, an effectively ranked feature list can be obtained, futher a compact and relevant feature subset can be directly generated from the ranked feature list. The experiments on 6 real-life benchmark datasets show that the classifiers induced by the algorithm of this paper has better or comparable classification performance than those established by Simba in most cases.
Starting Page	634
Ending Page	639
File Size	434641
Page Count	6
File Format	PDF
ISBN	9780769536439
DOI	10.1109/ISECS.2009.220
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	hypothesis-margin Mathematics Electronic commerce Data mining Computer science Filters Clustering algorithms Information security structure information Feature extraction Mathematical model Computer security feature selection
Content Type	Text
Resource Type	Article

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