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Bayesian Decision Theory for Dominance-Based Rough Set Approach

机译:基于优势的粗糙集方法的贝叶斯决策理论

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Dominance-based Rough Set Approach (DRSA) has been proposed to generalize classical rough set approach when consideration of monotonicity between degrees of membership to considered conepts has to be taken into account. This is typical for data describing vari-ous phenomena, e.g., "the larger the mass and the smaller the distance, the larger the gravity", or "the more a tomato is red, the more it is ripe". These monotonicity relationships are fundamental in rough set approach to multiple criteria decision analysis. In this paper, we propose a Bayesian decision procedure for DRSA. Our approach permits to take into account costs of misclassification in fixing parameters of the Variable Consistency DRSA (VC-DRSA), being a probabilistic model of DRSA.
机译:当必须考虑隶属度与所考虑的概念之间的单调性时,已提出基于优势的粗糙集方法(DRSA)来推广经典粗糙集方法。这通常用于描述各种现象的数据,例如“质量越大,距离越小,重力越大”,或者“西红柿越红,则越成熟”。这些单调关系是多准则决策分析的粗糙集方法的基础。在本文中,我们提出了一种针对DRSA的贝叶斯决策程序。我们的方法允许在固定变量一致性DRSA(VC-DRSA)的参数时考虑错误分类的成本,该变量是DRSA的概率模型。

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