NDLI: An Unbalanced Dataset Classification Approach Based on v-Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Yinggang Zhao Qinming He
Copyright Year	2006
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou (Yinggang Zhao; Qinming He)
Abstract	Support vector machine (SVM) has been extensively studied and has shown remarkable success in many applications. However, when faced with unbalanced datasets, the SVM can not get ideal classification result and even in some cases the classification ability was very bad and unaccepted. The V-support vector machine (V-SVM) is a new formulation of the regular SVM, and its parameter V has intuitive meanings compared with C (the penalty constant in SVM). By investigating the relation between SVM and V-SVM, we gave an equation between V and C, meanwhile we analyzed the factor behind the classification failure of SVM on unbalanced dataset. Then a classification algorithm based on V-SVM was addressed to overcome this inconvenience. Experimental results show the effectiveness of the proposed algorithm
Starting Page	10496
Ending Page	10501
File Size	172801
Page Count	6
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1714061
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Static VAr compensators Educational institutions Computer science Helium Application software Equations Failure analysis Classification algorithms Classification Support Vector Machine V-Support Vector Machine Unbalanced dataset
Content Type	Text
Resource Type	Article

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