Selective ensemble of SVDDs based on information theoretic learning

To make the traditional support vector data description (SVDD) achieve better generalization performance and more robust against noise, a selective ensemble method based on correntropy and Renyi entropy is proposed. In this proposed ensemble method, the correntropy between the radii of the basis classifiers and the radius of the ensemble is utilized to substitute the sum-squared-error (SSE) criterion. The Renyi entropy of the distances between the training samples and the center of ensemble is defined as the diversity measure for the proposed ensemble. Moreover, an ?1-norm based regularization term is introduced into the objective function of the proposed ensemble to implement the selective ensemble. Experimental results on synthetic and benchmark data sets show that the proposed ensemble strategy can achieve better performance than its related approaches.