Multi-class SVM with negative data selection for Web page classification
Chih‐Ming Chen, Hahn-Ming Lee, Ming-Tyan Kao · 2005
Support vector machine (SVM) has been demonstrated its excellent performance in terms of solving document classification problem. In this paper, SVM with one-against-all structure is applied to solve Web page classification problems with multi-class. However, the main problem of SVM with one-against-all structure is that the negative data might be too huge so that the training time obviously increase. To solve this problem, a negative data selection method is presented to reduce a large amount of negative data for SVM. Experimental results show that the training time is obviously reduced. Moreover, the proposed method also keeps a high accuracy rate for Web page classification.