A New Multi-class Classification Based on Non-linear SVM and Decision Tree
Jing Wang, Yong Fang Yao, Zhijing Liu · 2007
Decision tree is one common method used in data mining to extract predicted information. Based on Statistical Learning Theory (SLT), support vector machine(SVM) is a new kind of machine learning method that is used for classification and regression, it realizes the trade-off between empirical risk minimization(ERM) and generalization capability. SVM and decision tree have combined into one multi-class classifier so as to solve multi-class classification problems. In this paper, SVM is extended to non-linear SVM by using kernel functions and a new classification based on NSVM decision tree is proposed. Experiments show that the proposed method is effective and feasible.