A Fuzzy Classification of Web Pages Based on the Transposition-Learning Rule

When the overlap of categories is excessive, the accuracy of Web page classification decreases. In order to classify the Web pages accurately, a framework of fuzzy classification of Web pages is presented, to give a mechanism of combining the human knowledge on Web page classification by a member function. Then a general learning rule of the coefficients is proposed. The Lyapunov function is used to analyze the convergence of the general learning rule, and it is proved in theory that the general learning rule has the inherent factor which adjusts the coefficient values to gain the minimum error. On the basis of theoretic convergence analyses of a single-coefficient learning algorithm, a transposition rule is proposed, which is applied to the single-coefficient learning algorithm to gain quick convergence speed in the phase of coefficient learning. It is shown that both from the theoretic deduction of the learning convergence and from the experiment result, the fuzzy classification of Web pages is an efficient method.