A SVM Method for Web Page Categorization Based on Weight Adjustment and Boosting Mechanism

摘要

Web page classification is an important research direction of web mining. In the paper, a SVM method of web page classification is presented. It include four steps: (1) using analysis module to extract the core text and structural tags from a web page; (2) adopting the improved VSM model to generate the initial feature vectors based on the core text of web page; (3) adjusting weights of the selected features based on structural tags in web page to generate the base SVM classifier; (4) combining the base classifiers produced by iteration based on Boosting mechanism to obtain the target SVM classifier. The experiment of web page classification shows that the approach presented is efficient.