Document Classification with One-class Multiview Learning

Recently, automatic document classification has attracted a lot of attentions due to the large quantity of web documents. Amongst, a special case is to distinguish whether a document belongs to a target class (Directory) when only the documents of target class are given, which is a standard oneclass classification problem. Moreover, differed from other data, web pages have intrinsic (text) and extrinsic(hyperlink) features. Thus they are very suitable for multiview learning. To tackle the task of one-class document classification, a multiview one-class classifier is proposed, it utilizes the One-cluster clustering based data description (OCCDD) as the base one-class classifier, then gets a oneclass classifier in each view by setting a membership threshold, simultaneously, achieves the consensus of different views by a regularization term. Hereafter, different views boost each other, rather than ensemble the results independently or perform document recognition in single view case. We conduct the experiments on the standard WebKB dataset with OCCDD and the proposed multiview method. Experimental results show the good performance of the multiview method in terms of effectiveness and stability to parameter.