Diversified SVM Ensembles for Large Data Sets

Ivor W. Tsang, András Kocsor, James T. Kwok

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.