Abstract:

In order to improve the large-scale data adaptability of the support vector machine (SVM) algorithm, accelerate the classification speed of the SVM algorithm, one fast SVM classification method is proposed based on the decision tree. The focus of this method is to construct a decision tree and decompose the large-scale problem into relatively simple sub-problems, the tree nodes are composed by the linear SVMs, then each node contains a decision hyperplane, the classification process depends on the number of nodes. This method avoids using the nonlinear kernel function in classification of complex samples, and by using a linear kernel function, it needs not to undertake the model selection, thus accelerating the samples classification rate. Experiments show that for the nonlinear classification problem of large-scale data with multiple features, the method has higher speed than the traditional methods.