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Monotonic Variable Consistency Rough Set Approaches

机译:单调变量一致性粗糙集方法

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We consider new definitions of Variable Consistency Rough Set Approaches that employ monotonic measures of membership to the approximated set. The monotonicity is understood with respect to the set of considered attributes. This kind of monotonicity is related to the monotonicity of the quality of approximation, considered among basic properties of rough sets. Measures that were employed by approaches proposed so far lack this property. New monotonic measures are considered in two contexts. In the first context, we define Variable Consistency Indiscernibility-based Rough Set Approach (VC-IRSA). In the second context, new measures are applied to Variable Consistency Dominance-based Rough Set Approaches (VC-DRSA). Properties of new definitions are investigated and compared to previously proposed Variable Precision Rough Set (VPRS) model, Rough Bayesian (RB) model and VC-DRSA.
机译:我们考虑了可变一致性粗糙集方法的新定义,该方法对隶属集采用单调隶属度度量。关于所考虑的属性的集合理解单调性。这种单调性与近似质量的单调性有关,在粗糙集的基本属性中考虑。迄今为止,所提出的方法所采用的措施都缺乏这一特性。在两种情况下考虑了新的单调措施。在第一个上下文中,我们定义了基于可变一致性不可区分性的粗糙集方法(VC-IRSA)。在第二种情况下,新方法应用于基于可变一致性优势的粗糙集方法(VC-DRSA)。研究了新定义的属性,并将其与先前提出的可变精度粗糙集(VPRS)模型,粗糙贝叶斯(RB)模型和VC-DRSA相比较。

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