小样本条件下基于属性权重Shapley值分配的粗糙集决策模型
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南京航空航天大学

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中图分类号:

TP182

基金项目:

国家自然科学基金项目(面上项目)(72071111);国家科技部科技创新引智基地项目(G20190010178);中央高校基本科研业务费(NC2019003);南京航空航天大学研究生创新基地(实验室)开放基金项目(kfjj20200908)


Rough Set Decision-making Model based on Shapley Value Assignment of Attribute Weight under the Condition of Small Sample
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Affiliation:

Nanjing University of Aeronautics and Astronautics

Fund Project:

the National Natural Science Foundation of China (72071111);the Intelligence Introduction Base of the Ministry of Science and Technology (G20190010178);the Fundamental Research Funds for the Central Universities of China (NC2019003);the Open Fund of Postgraduate Innovation Base (Laboratory) at the Nanjing University of Aeronautics and Astronautics (kfjj20200908)

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    摘要:

    小样本条件下根据粗糙集理论构建的决策规则,受数据来源偶然性误差影响较大,个别数据样本难以反映真实知识关系。为解决小样本条件下粗糙集决策规则可信度未知的问题,提出信息区分量、属性影响方向等概念,运用Shapley值法进行进行属性权重分配,求取每个属性对决策结果的影响方向,进而得出决策规则的参考信度,以寻求真实可信且适合工程实际的决策规则。最后经实例论证了新方法的可行性以及对数据来源误差的分辨能力。

    Abstract:

    The decision rules based on rough set theory under the condition of small sample are greatly affected by the chance error of the initial data, and individual data samples are difficult to reflect the true knowledge relationship. To solve the problem of unknown reliability of rough set decision rules under the condition of small sample, concepts such as the amount of information distinction and the influence direction of attributes are proposed, and attribute weights are assigned using the Shapley value method. The influence direction of each attribute on the decision result is obtained, and the reference reliability of the decision rule is obtained to seek credible and suitable decision-making rules for engineering. Finally, the feasibility of the new method and the ability of discriminating the error of the data source are demonstrated through cases.

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  • 收稿日期:2020-12-08
  • 最后修改日期:2021-07-22
  • 录用日期:2021-07-30
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