NDLI: Constructing Least Square Support Vector Machines Ensemble Based on Fuzzy Integral

Content Provider	IEEE Xplore Digital Library
Author	Chun-Mei Liu Liang-Kuan Zhu
Copyright Year	2006
Description	Author affiliation: Coll. of Found. Sci., Harbin Univ. of Commerce (Chun-Mei Liu)
Abstract	Even the support vector machine (SVM) has been proved to improve the classification performance greatly than a single SVM, the classification result of the practically implemented SVM is often far from the theoretically expected level because they don't evaluate the importance degree of the output of individual component SVMs classifier to the final decision. This paper proposes a boosting least square support vector machine (LS-SVM) ensemble method based on fuzzy integral to improve the limited classification performance. In general, the proposed method is built in 3 steps: construct the component LS-SVM; obtain the probabilistic outputs model of each component LS-SVM; combine the component predictions based on fuzzy integral. The trained individual LS-SVMs are aggregated to make a final decision. The simulating results demonstrate that the proposed LS-SVM ensemble with boosting outperforms a single SVM and traditional SVM (or LS-SVM) ensemble technique via majority voting in terms of classification accuracy
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	2391
Ending Page	2395
File Size	235997
Page Count	5
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258731
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares methods Support vector machines Support vector machine classification Boosting Quadratic programming Voting Fuzzy control Educational institutions Business Electronic mail Information fusion LS-SVM SVM ensemble Fuzzy integral
Content Type	Text
Resource Type	Article

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