Reduction in decision table based on pair-wise complementarity of condition attributes

The reduction of attributes is a critical problem in the rough set theory. Finding the minimal reduct is turned out to be a NP-hard problem. Many heuristic algorithms, which use the significance of the condition attribute with reference to the decision attributes as the indication for attribute selection, have been proposed in this area. In this paper the pair-wise complementarity of condition attributes is defined based on conditional information entropy and employed as a heuristic in the attribute reduction process. Finally, a heuristic algorithm of reduction is proposed and tested on the UCI machine learning repository. It can be verified by the experimental results that the proposed algorithm is feasible and effective.