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Automatic Chinese Text Classification Based on NSVMDT-KNN

According to this paper, a novel approach based on non-linear support vetor machine decision tree (NSVMDT) and K nearest neighbors (KNN) is proposed towards Chinese text categorization. To begin with, SVM is extended to non-linear SVM by using kernel functions. And then the method of NSVMDT is presented based on traditional SVM decision tree. Furthermore, the KNN is combined with NSVMDT to solve the problem of the categorization of unbalanced Chinese texts sets. According to this method, experimental results have shown that the hybrid method based on NSVMDT and KNN could achieve better results than traditional SVM method for Chinese text categorization.