A Novel Approach to Naive Bayes Web Page Automatic Classification

In this paper, a novel approach of Web page classification using Naive Bayes (NB) classifier based on independent component analysis (ICA) is proposed. In order to perform the classification, a Web page is firstly represented by a vector of features with different weights, and the weight calculated method is improved. As the number of the features is big, principal component analysis (PCA) which is to select the relevant features will perform in preprocessing section as input for improved ICA algorithm (MFICA). Finally, the output of MFICA is sent to NB classifier for classification to boost the classifierpsilas performance. The experimental evaluation demonstrates that the NB classifier based on ICA model provides acceptable classification accuracy.