Double-quantitative decision-theoretic rough set
Double-quantitative decision-theoretic rough set
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DOI：
10.1016/j.ins.2015.04.020
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发表时间：
2015-09
期刊：
影响因子：
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通讯作者：
Wentao Li;Weihua Xu
中科院分区：
文献类型：
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作者：
Wentao Li;Weihua Xu
概率粗糙集（PRS）和分级粗糙集（GRS）分别是度量等价类与基本概念之间相对和绝对数量信息的两种量化模型。决策粗糙集（DTRS）作为一种特殊的PRS模型，主要利用条件概率来表示相对量化。但是，它忽略了等价类与基本集合重叠的绝对量化信息，不能反映信息的区分程度，使其在真实的生活中的应用极为狭窄。为了克服这些缺陷，本文提出了一种基于贝叶斯决策过程和GRS的双定量决策理论粗糙集（Dq-DTRS）框架。建立了两种Dq-DTRS模型，其本质是相对量化和绝对量化。在进一步研究了决策规则和两种模型之间的内在联系后，本文以医学诊断为例对理论进行了解释和表达，这对于将理论应用于实际问题具有一定的参考价值。
The probabilistic rough set (PRS) and the graded rough set (GRS) are two quantification models that measure relative and absolute quantitative information between the equivalence class and a basic concept, respectively. As a special PRS model, the decision-theoretic rough set (DTRS) mainly utilizes the conditional probability to express relative quantification. However, it ignores absolute quantitative information of the overlap between equivalence class and the basic set, and it cannot reflect the distinctive degrees of information and extremely narrow their applications in real life. In order to overcome these defects, this paper proposes a framework of double-quantitative decision-theoretic rough set (Dq-DTRS) based on Bayesian decision procedure and GRS. Two kinds of Dq-DTRS model are constructed, which essentially indicate the relative and absolute quantification. After further studies to discuss decision rules and the inner relationship between these two models, we introduce an illustrative case study about the medical diagnosis to interpret and express the theories, which is valuable for applying these theories to deal with practical issues.