Text Feature Ranking Based on Rough-set Theory

With the aim to reduce the dimensionality without sacrificing classification performance, the author gains insights from attribute reduction based on discernibility matrix in rough-set theory and proposes two text feature selection algorithms, i.e., DB1 and DB2. The experimental results indicate that DB2 not only yields much higher accuracy than information gain when the number of features is smaller than 6000, but also incurs much smaller CPU time than information gain.