Matrixized Multi-class Cost Sensitive Classification Mode

At present,most of the classifiers are evaluated by classification accuracy,which assumes that all the misclassification costs are the same.Actually,different misclassification may bring different loss.Therefore,the cost sensitive learning has been becoming a hot research area in pattern recognition.By combining the cost sensitive and matrixized learning thoughts,this paper proposes a matrixized multi-class cost sensitive classification mode.The experimental results on the data show that the proposed method can reduce the classification costs and improve the classification accuracy of the minority or higher cost classes.Meanwhile,the proposed method has a better convergence,which illustrates the effectiveness and practice of the proposed method.