Ensemble learning with generalization performance measurement and negative correlation

Conventional ensemble learning algorithms based on ambiguity decomposition and negative correlation learning theory are carried out on the basis of empirical risk minimization principle. When SVM is used as the component learner, the generalization ability of ensemble learning system may not be improved. In this paper, based on the estimation of the generalization performance of SVM and negative correlation learning theory, a new selective ensemble SVM learning method is proposed. Experiments on real world data sets from UCI were carried out to demonstrate the effectiveness of this method.