A novel approach of feature classification using Support Vector Data Description combined with interpolation method
Chi-Kai Wang, Yung Ting, Yi‐Hung Liu · 2008
In this paper, we propose a novel approach to feature classification using Support Vector Data Description (SVDD) combined with interpolation method. In SVDD, the width parameter s and the penalty parameter C influence the learning results. The N-fold M times cross-validation method 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 the interpolation method to generalize new outliers. At the last, we use four benchmark data sets: Iris, Wine, Balance-scale, and Ionosphere four data base to validate the method in this research has better classification output and faster performance.