Selective ensemble of SVDDs with Renyi entropy based diversity measure

In this paper, a novel selective ensemble strategy for support vector data description (SVDD) using the Renyi entropy based diversity measure is proposed to deal with the problem of one-class classification. In order to obtain compact classification boundary, the radius of ensemble is defined as the inner product of the vector of combination weights and the vector of the radii of SVDDs. To make the center of ensemble achieve the optimal position, the Renyi entropy of the kernelized distances between the images of samples and the center of ensemble in the high-dimensional feature space is defined as the diversity measure. Moreover, to fulfill the selective ensemble, an ℓ₁-norm based regularization term is introduced into the objective function of the proposed ensemble. The optimal combination weights can be iteratively obtained by the half-quadratic optimization technique. Experimental results on two synthetic data sets and twenty benchmark data sets demonstrate that the proposed selective ensemble method is superior to the single SVDD and the other four related ensemble approaches.