基于类别的优势原则粗糙近似
Class-based Rough Approximation with Dominance Principle
摘要：
基于优势的粗糙集方法（DRSA）作为Pawlak粗糙集理论的推广，在多准则决策分析（MCDA）中具有重要的应用价值。在以前的DRSA模型中，上近似和下近似的定义是保留类联合而不是单例类。本文针对经典DRSA模型、VC-DRSA模型和VP-DRSA模型，提出了一种新的基于类的粗糙近似方法。此外，研究了新的基于类的约简。
英文摘要：
Dominance-based Rough Set Approach (DRSA), as the extension of Pawlak's Rough Set theory, is effective and fundamentally important in Multiple Criteria Decision Analysis (MCDA). In previous DRSA models, the definitions of the upper and lower approximations are preserving the class unions rather than the singleton class. In this paper, we propose a new Class-based Rough Approximation with respect to a series of previous DRSA models, including Classical DRSA model, VC-DRSA model and VP-DRSA model. In addition, the new class-based reducts are investigated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
