Parameterized Local Reduction of Decision Systems

[摘要] 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 ofθ-local reduction in order to offer a minimal description for specialθ-possible decision rules. The approach of discernibility matrix is employed to investigate the structure of aθ-local reduction and compute allθ-local reductions. An example of medical diagnosis is employed to illustrate our idea of theθ-local reduction. Finally, numerical experiments are performed to show that our method proposed in this paper is feasible and valid.

[学科分类] 应用数学