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A New Criterion for Attribute Reduction Based on Variable Precision Rough Set Model

机译:基于可变精密粗糙集模型的属性降低的新标准

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摘要

The rule set which is acquired based on rough set theory can be classified into two categories: deterministic rules and probabilistic rules. Traditional attribute reduction definitions in variable precision rough set model cannot guarantee the rule properties, namely deterministic or probabilistic. In this paper, a new criterion for attribute reduction is put forward based on variable precision rough set model. The rule properties can be preserved during the process of attribute reduction. The relationships between the new reduct definition and available definitions, including Ziarko's reduct definition and β lower distribution reduct definition are also discussed.
机译:基于粗糙集理论获取的规则集可以分为两类:确定性规则和概率规则。可变精度粗糙集模型中的传统属性缩减定义无法保证规则属性,即确定性或概率。本文基于可变精度粗糙集模型提出了一种属性减少的新标准。可以在属性减少过程中保留规则属性。还讨论了新的还原定义和可用定义之间的关系,包括Ziarko的还原定义和β降低分布的定义。

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