Diversified SVM ensembles for large data sets

Authors:
Ivor W. Tsang;Andras Kocsor;James T. Kwok
Affiliations:
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;Research Group on Artificial Intelligence, Hungarian Academy of Sciences and University of Szeged, Szeged, Hungary;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Venue:
ECML'06 Proceedings of the 17th European conference on Machine Learning
Year:
2006

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Abstract

Recently, the core vector machine (CVM) has shown significant speedups on classification and regression problems with massive data sets. Its performance is also almost as accurate as other state-of-the-art SVM implementations. By incorporating the orthogonality constraints to diversify the CVM ensembles, this turns out to speed up the maximum margin discriminant analysis (MMDA) algorithm. Extensive comparisons with the MMDA ensemble along with bagging on a number of large data sets show that the proposed diversified CVM ensemble can improve classification performance, and is also faster than the original MMDA algorithm by more than an order of magnitude.