Structured probabilistic rough set approximations

Structured probabilistic rough set approximations
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结构化概率粗糙集近似

DOI:
10.1016/j.ijar.2017.08.004
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发表时间:
2017-11
影响因子:
3.9
通讯作者:
Pan Xiaochen
Pan Xiaochen
中科院分区:
计算机科学2区
文献类型:
--
作者:
Ma Jianmin;Zou Cunjun;Pan Xiaochen

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提出了基于条件概率的概率粗糙集近似,以描述等价类和近似集之间期望的精度水平。该定义显示了满足某些条件的个体的详细信息,但忽略了结构信息。本文将粗粒度论域中的结构化粒应用于粗粒度论域中,给出了粗粒度论域和原论域的幂集之间的结构化概率粗糙集近似.利用放大和结构化概率粗糙近似算子,研究了同一论域上的两对概率粗糙下近似和上近似。研究了它们的相关性质和相互关系。此外,应用贝叶斯决策过程,条件概率和损失函数,三路分类的结构概率粗糙集近似,然后提出了分类的粗粒度论域的结构化颗粒分为三个不相交的结构概率区域。该方法给出了阈值对的值。同时,利用最小风险决策规则,我们还可以构造结构化的概率粗糙下近似和上近似。最后,我们讨论了结构概率正区域和结构概率负区域的单调性。
Probabilistic rough set approximations are proposed based on a conditional probability to describe the desired levels of precision between the equivalence classes and an approximated set. This definition shows the detailed information on individuals satisfying some conditions but ignores the structural information. In this paper, applying the structured granules in a coarsened-grained universe, we introduce structured probabilistic rough set approximations between the power sets of the original universe and the coarsened-grained universe. By using the zooming-in and structured probabilistic rough approximation operators, two pairs of probabilistic rough lower and upper approximations on the same universe are investigated. Related properties and relationships of them are investigated. Furthermore, applying the Bayesian decision procedure, conditional probability and loss functions, three-way classifications in structured probabilistic rough set approximations are then proposed to classify the structured granules of the coarsened-grained universe into three disjoint structured probabilistic regions. This method gives the values of the pair of thresholds. Meanwhile, by using the minimum-risk decision rules, we also can construct the structured probabilistic rough lower and upper approximations. Finally, we discuss the monotonicity of structured probabilistic positive and negative regions.
两个宇宙上概率粗糙集与贝叶斯风险决策的关系
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