A topic-specific data filtering framework based on rough set theory
Hong Guo, Yunda Cao, Song Guo · 2004
With the tremendous growth in the volume of text documents available on the Internet and digital libraries, accurate specific topic text filtering is needed. In this paper we propose a rough set aided method to reduce the dimensionality of feature vectors. In order to extract accurate features, we also provide a novel filtering technique called twice-filtering to treat with two different feature sets: "interkeywords" and "intrakeyword". A simple application of E-mail filtering system based on our topic-specific filtering technology shows that with the incorporation of variant weighting methods and more accurate features extracted, our filtering algorithm can speed up the filtering operation with a high precision and recall.