Neural networks training based on differential evolution in radial basis function networks for classification of web logs

Abstract

With the fastest growth of World Wide Web it is quite difficult to track and understand users' need for the owners of a website. Hence, an intelligent analyzer is proposed to find out the browsing patterns of a user. Moreover the pattern, which is revealed from this deluge of web access logs must be interesting, useful, and understandable. In this paper, a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed to classify the web pages on time of access and region of access. In phase one a meta-heuristic approach known as differential evolution is used to reveal the parameters of the modified kernel. The second phase focus on optimization of weights for learning the networks. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy vis-à-vis radial basis function neural networks. © 2013 Springer-Verlag Berlin Heidelberg.