An Attribute Selection Approach and Its Application

In this paper we propose an attribute selection approach, which is based on rough sets theory. The main feature of this method is that it not only takes the dependency degree of decision attributes on condition attributes into account, but also considers decision makers´ priori knowledge about importance of condition attributes to decision attributes. It combines these two factors as a criterion of attribute selection. In addition, it uses a compound weights algorithm to implement a proper reduct. As a result, the most effective attribute subset is obtained, and a practical, reduced knowledge rule set can be acquired. In order to judge the effectiveness of the proposed approach, the knowledge rule set acquired is applied to a prototype simulation system of a part assembly cell for optimum control. Experimental results indicate that the attribute and reduct selection approach is more effective