Study of Web page classification based on graph-based semi-supervised learning
Zhou Jian-zhong · Jisuanji yingyong yanjiu · 2008
This paper proposed a graph-based semi-supervise learning method,and applied to the Web document classification.Used k-nearest neighbor algorithm to construct a weighted graph with edge weights representing the similarity between the nodes,and the nodes in the graph were labeled and unlabeled Web pages.In order to use unlabeled data to help classification and get higher accuracy,computed edge weights of the graph through combining weighting schemes and link information of Web pages.By using probabilistic matrix methods and belief propagation,the labeled nodes pushed out labels through unlabeled nodes.The learning problem was then formulated in terms of label propagation in a graph.Experiments on the WebKB dataset indicate that the graph-based semi-supervise learning method can improve the effectiveness of Web document classification.