A Novel Approach to Feature Selection Using PageRank algorithm for Web Page Classification

In this paper, a novel filter-based approach is proposed using the PageRank algorithm to select the optimal subset of features as well as to compute their weights for web page classification. To evaluate the proposed approach multiple experiments are performed using accuracy score as the main criterion on four different datasets, namely WebKB, Reuters-R8, Reuters-R52, and 20NewsGroups. By analyzing the obtained results, it is observed that the accuracy score of the classifier on WebKB, Reuters-R8, and Reuters-R52 datasets significantly improved from 91% up to 96% compared to the best result achieved by other feature selection methods like IG and Chi-2. Whereas, the accuracy score of the classifier on 20NewsGroups dataset didn't see any noticeable improvement and remained close to the most compared methods. Evaluating the performance of the proposed approach shows the superiority of it in obtaining higher accuracy scores when compared with the feature sets selected by other methods.