Abstract: In recent years, more and more attention has been paid to the attribute reduction algorithm of rough set, especially the heuristic reduction algorithm. In order to measure the attribute importance, people used different kinds of information entropy model in rough set, and obtained many reduction algorithms on the basis of the theory of information entropy to solve the problem of attribute reduction of rough set. However, there are a number of problems in the existing information entropy methods. To solve these problems, this paper firstly combines the knowledge granularity and relative decision entropy, and introduces a new information entropy model—the granularity decision entropy. Then, using the granularity decision entropy to measure the importance of attributes, the new reduction algorithm—ARGDE reduction algorithm is obtained. Finally, different UCI data sets are used to perform the experiment, and the algorithm can get better results by comparing with the existing reduction algorithms.

Key words: granular decision entropy, relative decision entropy, knowledge granularity, attribute reduction, rough sets