One-sided Support Vector Regression for Multiclass Cost-sensitive Classification
Han-hsing Tu, Hsuan-Tien Lin · 2010
We propose a novel approach that reduces cost-sensitive classification to one-sided re-gression. The approach stores the cost infor-mation in the regression labels and encodes the minimum-cost prediction with the one-sided loss. The simple approach is accompa-nied by a solid theoretical guarantee of er-ror transformation, and can be used to cast any one-sided regression method as a cost-sensitive classification algorithm. To validate the proposed reduction approach, we design a new cost-sensitive classification algorithm by coupling the approach with a variant of the support vector machine (SVM) for one-sided regression. The proposed algorithm can be viewed as a theoretically justified extension of the popular one-versus-all SVM. Experimen-tal results demonstrate that the algorithm is not only superior to traditional one-versus-all SVM for cost-sensitive classification, but also better than many existing SVM-based cost-sensitive classification algorithms. 1.