A Study on Reduction of Attributes Based on Variable Precision Rough Set and Information Entropy

As a powerful tool for inducing classification knowledge from databases, rough set theory can be used to reduce attributes without the requirement of external information. In previous research, the approximation quality gamma is usually used as a criterion in rough set based reduction. But the gamma criterion is of limited value when the relationship between attributes is disturbed by noise. Inspired by previous research, this paper proposes an improved criterion for the reduction of attributes based on variable precision rough set and information entropy. Compared with the gamma criterion, this criterion could gain more tolerance of inconsistency, randomness and noise. A coefficient of correlation for this criterion indicated by epsiv is also proposed in order to make the evaluation more reasonable