Web Classification Based on Tolerance Rough Set
The efficient and precise Web classification is necessary for fully using the rich resource . The application of well-known machine learning technology,including K-Nearest Neighbor, Naive Bayes and etc., have brought out the automatic and semi-automatic Web classification. But many of Web classification algorithms considered classes of mutually exclusive concepts , few took the fact of overlap concept between classes into account. In fact , a single page usually falls into several categories. That is to say there exit indiscernible relation between classes. Rough sets theory was first presented by Pawlak professor in 1982, which was a prefect tool that denoted indiscernible relation between sets. The classification with application of rough set has proved a more prefect precision method. In this article ,with Web document in Vector Space Model, an algorithm for Web classification based on Tolerance Rough Set was presented , which made use of the characteristic item co-occurrence to enrich description of Web document. As a result ,a appropriate way to Web representation and a good performance improving the Web classification precision were gained by experiment.