An Attribute Reduction Algorithm in Rough Set Theory Based on Information Entropy

Rough set theory is an effective approach to imprecision, vagueness and incompleteness in classification analysis and knowledge discovery. Attribute reduction and relative attribute reduction are the core of KDD. From the point of view of information, the basic concepts of rough set were analyzed in this paper. A novel attribute reduction algorithm was constructed by adopting conditional entropy and the improved importance of attribute. This algorithm does not calculate the attribute core but directly reduces the original attribute set. The performance of this algorithm was compared with that of the old algorithm based on mutual information by using some classical databases in the UCI repository. Finally, the validity and the feasibility of the algorithm are demonstrated by the experiment results.