Parameterized Local Reduction of Decision Systems

Abstract

One important and valuable topic in rough sets is attribute reduction of a decision system. The existing attribute reductions are designed to just keep confidence of every certain rule as they cannot identify key conditional attributes explicitly for special decision rules. In this paper, we develop the concept of $\theta$-local reduction in order to offer a minimal description for special $\theta$-possible decision rules. The approach of discernibility matrix is employed to investigate the structure of a $\theta$-local reduction and compute all $\theta$-local reductions. An example of medical diagnosis is employed to illustrate our idea of the $\theta$-local reduction. Finally, numerical experiments are performed to show that our method proposed in this paper is feasible and valid.