在粗糙集理论基于近似的分布约简算法中，当前主要有基于上（下）近似、基于

在粗糙集理论基于近似的分布约简算法中，当前主要有基于上（下）近似、基于最优近似，这在实际应用中存在一定的局限性，更多情况下应考虑在任意近似基础上来讨论分布约简结果，以满足不同应用需求.为此提出粗糙集的单侧近似的概念，建立基于单侧近似的粗糙集模型，讨论粗糙集单侧近似的性质以及粗糙集单侧近似与最优近似之间的关系,并在此基础上提出了单侧近似约简和单侧近似分布约简的概念.

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结果 (简体中文) 2:[复制]

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Among the approximate distribution reduction algorithms based on rough set theory, there are mainly upper (lower) approximation and optimal approximation, which have some limitations in practical application. In more cases, it should be considered to discuss the distribution reduction results on the basis of any approximation to meet different application requirements. Therefore, the concept of one-sided approximation of rough set is proposed, A rough set model based on unilateral approximation is established. The properties of unilateral approximation of rough set and the relationship between unilateral approximation and optimal approximation of rough set are discussed. On this basis, the concepts of unilateral approximate reduction and unilateral approximate distribution reduction are proposed<br>

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结果 (简体中文) 3:[复制]

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At present, distribution reduction algorithms based on approximation in rough set theory are mainly based on upper (lower) approximation and optimal approximation, which have some limitations in practical application. In more cases, distribution reduction results should be discussed on the basis of arbitrary approximation to meet different application requirements. Therefore, the concept of one-sided approximation of rough set is put forward, a rough set model based on one-sided approximation is established, and the properties of one-sided approximation of rough set and the relationship between one-sided approximation and optimal approximation of rough set are discussed.

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