Sorting Decision Model for Dynamic Fault Tolerance Based on Dominance Relation Rough Set
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摘要: 为提高优势关系粗糙集模型在分级决策问题中的容错能力,将容错处理视为可动态调整的过程,根据用户向上、向下和综合两者的3种偏好趋向,提出了3种对应的分级算法,对边界域对象进行初始分级,利用对象的覆盖信息作为启发式知识调整其分级决策的结果,实现正确分级或接近正确分级.与变一致性优势关系粗糙集模型相比,不需要事先根据经验确定和调整阈值.案例应用结果表明:本文提出的3种偏好情况下的分级正确率比现有的分级算法平均提高了21.34%,对应的误分总代价平均降低了50.91%.Abstract: To enhance the fault-tolerant capacity of the dominance relation rough set model in solving sorting decision problems, three efficient sorting decision algorithms are proposed by regarding the fault-tolerant processing as a dynamic adjusting process according to the fault tolerance direction of the user's preference, i.e., upward, downward, or synthesis of the both. The boundary objects are initially ranked by the proposed algorithms, and the obtained results are adjusted using the coverage information as the heuristic criteria to achieve a accurate or near accurate sorting of the object finally. In contrast to the variable-consistency dominance-based rough set approach (VC-DRSA), the proposed algorithms do not need prior domain knowledge to determine and adjust a threshold. Application of the algorithms to wine quality dataset show that the proposed methods can achieve a 21.34% improvement in average sorting accuracy and a 50.91% reduction in average mis-sorting cost, compared with the existing methods.
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Key words:
- rough set /
- dominance relation /
- fault tolerance /
- sorting /
- decision making
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