A Positive Approximation Set Based Accelerating Approach for Condition Attribute Reduction

In this paper, the specific framework and characteristics of positive approximation set are investigated. A novel attribute reduction algorithm is introduced for accelerating the existing reduction approaches for incomplete decision table. Consequently, a tolerance relation based algorithm (IFSPA) is proposed and employed to improve the performance of existing heuristic reduction algorithms. A series of experiments using real-life data sets are carried out to verify the effectiveness of the improved algorithms and compare their efficiency with that of the original algorithms. The simulation results prove the fact that the improved algorithms would output exactly the same reducts as the original ones, while the improved ones could accomplish the reduction task in a shorter time and even in a more stable method.