Knowledge Reduction and its Rough Entropy Representation of Decision Tables in Rough Set

The disadvantages of the recent reduction algorithms are analyzed deeply. A new measure to knowledge and rough set is introduced to discuss the rough entropy of knowledge and the roughness of rough set. Based on this entropy, the new significance of attribute is defined and a heuristic algorithm of knowledge reduction is proposed and compared with two methods of attribute reduction which are based on the positive region and the conditional information entropy respectively. The result shows that the proposed heuristic information is better and more efficient than the others, and is greatly effective and feasible in searching the minimal or optimal reduction. Theoretical analysis and experimental results indicate that the time complexity of this reduction algorithm is less than that based on the current positive region and the conditional information entropy.