Applying a novel combined classifier for pornographic web filtering in a grid computing environment

As the Web expands exponentially, there are a flood of pornographic Web sites on the Internet. Thus effective and fast web filtering systems are essential. Web filtering based on hypertext classification has become one of the important techniques to handle and filter inappropriate information on the Web. The task involved can be parallelized and distributed in a grid environment. However, how to improve the performance of the hypertext classification under the situation of noisy data is still a challenging problem. In this paper, we propose a new approach for hypertext classification in Web filtering, which uses a novel support vector machine and k-nearest neighbor (KNN-SVM) to remove noisy training examples. The task of text categorization is distributed in several computers. The experimental results show that the generalization performance in the accuracy of classification and the processing time are improved significantly compared to that of the traditional SVM classifier over the grid, and adapt to engineering applications.