Analysis of Attribute Reduction of Incomplete Decision Table Based on Information Entropy

Attribute reduction of incomplete decision table is one of the important contents of Rough Set theory. The paper presents an attribute reduction algorithm of incomplete decision table based on information entropy, where the attribute reduction method based on information entropy is studied and analyzed. In the proposed method, the relative core of decision table is treated as the starting point. And then the concept of entropy is employed as heuristic information and conditions of reduction for seeking attribute reduction with a bottom-up approach. Finally, experimental results verify the feasibility and effectiveness of this method.