A Rough Set-Based Approach to Text Classification

Abstract

A non-trivial obstacle in good text classification for information filtering and retrieval (IF/IR) is the dimensionality of the data. This paper proposes a technique using Rough Set Theory to alleviate this situation. Given corpora of documents and a training set of examples of classified documents, the technique locates a minimal set of co-ordinate keywords to distinguish between classes of documents, reducing the dimensionality of the keyword vectors. This simplifies the creation of knowledge-based IF/IR systems, speeds up their operation, and allows easy editing of the rule bases employed. The paper describes the proposed technique, discusses the integration of a keyword acquisition algorithm with a rough set-based dimensionality reduction algorithm, and provides experimental results of a proof-of-concept implementation.