Improved web page identification method using neural networks

Abstract

In this paper, an improved web page classification method (IWPCM) using neural networks to identify the illicit contents of web pages is proposed. The proposed IWPCM approach is based on the improvement of feature selection of the web pages using class based feature vectors (CPBF). The CPBF feature selection approach has been calculated by considering the important term's weight for illicit web documents and reduce the dependency of the less important term's weight for normal web documents. The IWPCM approach has been examined using the modified term-weighting scheme by comparing it with several traditional term-weighting schemes for non-illicit and illicit web contents available from the web. The precision, recall, and F1 measures have been used to evaluate the effectiveness of the proposed IWPCM approach. The experimental results have shown that the proposed improved term-weighting scheme has been able to identify the non-illicit and illicit web contents available from the experimental datasets.