Graph Based Co-Training Algorithm for Web Page Classification
Licheng Jiao · Dianzi xuebao · 2009
This paper proposes a novel inductive semi-supervised algorithm for web page classification named GCo-training,exploiting texts in web pages and hyperlinks among them.GCo-training iteratively trains two classifiers-a graph-based semi-supervised classifier based on hyperlinks among web pages and a Bayes classifier based on texts in web pages,under the framework of Co-training.On the one hand,the graph-based semi-supervised classifier obtains high accuracy based on a small set of labeled examples through exploiting links among web pages and can augment labeled examples for the Bayes classifier.On the other hand,the Bayes classifier can also provide labeled example for the graph-based classifier after it learning on labeled set augmented by the graph-based classifier.Therefore,the two classifiers help each other and improve their respective performance during the process of training.Finally,the Bayes classifier can classify a large number of unseen examples.We test GCo-training algorithm,Co-training algorithm based on words occurring on web pages and words occurring in hyperlinks and Bayes algorithm based on EM on the Web→KB dataset.Experimental results show GCo-training performs much better than the other algorithms.