Search

This paper studies the problem of building Web page classifiers using positive and unlabeled examples, and proposes a more principled technique to solving the problem based on tolerance rough set and Support Vector Machine (SVM). It uses tolerance classes to approximate concepts existed in Web pages and enrich the representation of Web pages, draws an initial approximation of negative example. It then iteratively runs SVM to build classifier which maximizes margins to progressively improve the approximation of negative example. Thus, the class boundary eventually converges to the true boundary of the positive class in the feature space. Experimental results show that the novel method outperforms existing methods significantly. [ABSTRACT FROM AUTHOR]

The theory of rough sets is an extension of the set theory, for the study of intelligent systems characterized by insufficient and incomplete information. Since proposed by Pawlak, rough sets have evoked a lot of research. Theoretic study has included algebra aspect of rough sets. In paper [1] the concept of rough group and rough subgroup was introduced, but with some deficiencies remaining. In this paper, we intend to make up for these shortages, improve definitions of rough group and rough subgroup, and prove their new properties. [ABSTRACT FROM AUTHOR]