Build decision tree on support vector machine

C4.5 is a popular classification method which can give the explainable and intuitional classification rules. But it is prone to overfitting due to the data noise or the distribution of the instances. In this paper, we proposed a new decision tree method with the support vector machine (SVM-DTR), which make the surface of the decision tree to discriminate the instances from the different categories as far as possible. SVMis used to measure the importance of the attribute on the fact that the cosine of the angle between the attribute axis and the normal of the decision surface can quantize its significance. Similar as the C4.5, each time we choose the most important attribute as the root of the sub-tree. We analyze the influence of the kernel width to the magnitude of the gradient and obtain the empirical settings about the kernel width from the experiments. The comparisons between the SVM-DTR and the C4.5 on 5 datasets from UCI machine learning repository show that SVM-DTR achieve the better performance than C4.5.