A novel approach to generate artificial outliers for support vector data description

In this paper, we propose a novel approach to generate artificial outliers for support vector data description with boundary value method. In SVDD, the width parameter s and the penalty parameter C influence the learning results. The N-fold M times cross-validation is well-known and popular scheme to calculate the best (C , s ) values. To automatically optimize the identification rate, we need more outliers. Due to this reason, we utilize boundary value in any two dimensions randomly to generalize new outliers. At the last, we use three benchmark data sets: Iris, Wine, and Balance-scale data base to validate the approach in this research has better classification result and faster performance.