决策系统中核心的实用算法

Xin-Ying Chen, Guan-yu Li, Jiahong Yan
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引用次数: 0

摘要

核心计算作为属性约简的基础,是粗糙集理论的关键问题之一。实际上,决策系统是不断变化的,这就导致了系统的不一致性。为了解决核心计算前的问题,基于桶排序和等价类划分,提出了一种时间复杂度为0 (|P||U|)的新算法。在此基础上,提出了一种使宇宙一致性和小型化的有效方法。根据相关定理和等价命题,提出了一种基于不协调指标的核心算法,其时间性能为0 (|C|2|U/C|)。理论分析和实验表明,本文提出的方法不仅简化了相关操作,而且能给出准确的核心结果。
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The practical algorithm for core in decision system
As the basis of attribute reduction, core computation is one of the key issues in rough set theory. Actually, decision system is continuously changing, which leads to the inconsistent system. To deal with the problem before core computation, based on bucket sort and equivalence class partition, a new algorithm with time complexity of O(|P||U|) is proposed. After that, an effective approach is proposed to make universe consistent and smaller. According to involved theorems and equivalent propositions, an algorithm for core on the discordant index is proposed also and its time performance is O(|C|2|U/C|). The theoretical analysis and experiment show that the ways proposed here not only can simplify the relevant operations but also can give accurate core results.
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