An empirical comparison of ensemble classification algorithms with support vector machines

The trained support vector machine (SVM) classifiers are used as basic classifiers and the ensemble methods for creating ensemble classifier, such as bagging and boosting, etc., are evaluated on two data sets.

Abstract

An ensemble classifier often has better performance than any of the single learned classifiers in the ensemble. In this paper, the trained support vector machine (SVM) classifiers are used as basic classifiers. The ensemble methods for creating ensemble classifier, such as bagging and boosting, etc., are evaluated on two data sets. Some conclusions are obtained. Bagging with SVM can stably improve classification accuracy, while the improvement obtained by boosting with SVM is not obvious. These two methods largely increase space complexity and time complexity. Comparatively, the multiple SVM decision model, training individual SVM classifiers using training subsets obtained by partitioning the original training set, has a better trade-off between the classification accuracy and efficiency.