Abstract:

Web page classification is an extended hot field for solving the problem of information overload ,with the excellent ability to learn, support vector machine shows a specific advantage in solving high dimensional problems. A new classification algorithm based on the combination of support vector machine and improved immune clone is proposed after the research of support vector machines and standard immune clones. As the standard algorithm achieves antibody variants through inverting  randomly some bits in antibody coding, so it is not strong in searching capability, for this deficiency, the paper distinguishes memory units and normal units, defines adaptive probability for the memory units, thereby strengthens search capability in the neighborhood of the current optimal solution, thus accelerates the speed to find the global optimal solution. A lot of experiments have shown that the improved algorithm which has a better parameters selection effect and a higher efficiency is a web page classification method with high accuracy and efficiency.

Key words: Web page classification;support vector machine;feature extraction;parameter selection;immune algorithm