基于属性重要度的风险决策粗糙集属性约简

基于Pawlak 粗糙集的属性约简一般保持决策表的正区域不变, 然而由于现实中不同用户对不同约简精度的需求, 获取属性值的实际代价与个人偏好可能不同. 针对决策者主观个人偏好、客观约简精度、获取属性值的实际代价和决策表各区域的误判代价等综合情况, 提出新的约简算法, 并讨论约简代价与约简精度间的关系. 通过遗传算法, 采用启发式方法搜索出局部最优约简子集. 仿真实验表明, 所提出的算法操作性强, 更适合处理实际决策问题.

Risk DTRS attribute reduction based on attribute importance

Generally, when talking about attribute reduction of a decision table, it usually keeps the positive region unchanged based on the Pawlak’s rough sets theory. However, the needs may be different for different precision of the reduction in real life as well as the actual cost to obtain attribute values and personal preferences. Based on the risk of personal preference for the subjective aspect, the accuracy of reduction, the actual cost of obtaining attribute value, and the risk of interval misjudgment for the objective aspects, a novel attribute reduction algorithm is proposed. Then, the relationship between the reduction cost and the reduction accuracy is discussed. Based on the genetic algorithm, a heuristic method for searching the local optimal reduction subset is proposed. Simulation experiments show that the algorithm is feasible, and more realistic to deal with practical decision-making problems.