基于Pawlak的决策粗糙集的属性约简研究

Researh on Attribute Reduction of Decision-theoretic Rough Set Model Based on Pawlak

粗糙集用于规则归纳时,其正域规则和边界规则这两种不同的分类规则会导致不同的决策序列。这两种分类规则都能够从语法和语义上进行区分,并被Pawlak模型所延伸的粗糙集理论所解释。属性约简是粗糙集理论的一个重要概念,本文针对决策粗糙集中的决策单调性这个分类属性,给出属性约简中基于正域约简模型及其分析。

Rough set theory can be applied to rule induction.There are two different types of classification rules,positive and boundary rules,which leading to different decisions and consequences.They can be distinguished from the syntax measures and semantic measures.Both the two can be interpreted by a probabilistic extension of the Pawlak rough set model.Attribute reduction is an important concept of rough set theory.This paper addresses attribute reduction in decision-theoretic rough set models regarding the classification properties of decision-monotonicity and provides a positive-based Veduction model in attribute reduction and its analysis.