基于多粒度的多源决策信息系统的最优选择
Optimal Selection of Multi-Source Decision Information Systems Based on Multi-Granularity
DOI: 10.12677/isl.2025.96115, PDF,   
作者: 石孝安*:青海职业技术大学公共教育学院,青海 西宁;青海师范大学计算机学院,青海 西宁;傅 丽:青海民族大学数学与统计学院,青海 西宁;冶忠林, 王伟杰:青海师范大学计算机学院,青海 西宁
关键词: 多粒度多源信息系统属性约简最优选择Multi-Granularity Multi-Source Information System Attribute Reduction Optimal Selection
摘要: 多粒度思想是近些年来处理复杂数据的常用方法,受多粒度思维的启发,本文结合多粒度标记信息系统的概念,提出了多源决策信息系统的广义决策函数的定义;然后利用广义决策函数讨论了协调和不协调的多源决策信息系统的最优选择以及局部最优选择,并讨论了局部相对约简。同时,给出了相应的例子来验证所提出方法的可行性。
Abstract: The multi-granularity thinking is a common method for handling complex data in recent years. Inspired by the multi-granularity thinking, this paper combines the concept of multi-granularity labeled information systems and proposes the definition of the generalized decision function of multi-source decision information systems. Then, the generalized decision function was used to discuss the optimal selection and local optimal selection of coordinated and uncoordinated multi-source decision information systems, and the local relative reduction was also discussed. Meanwhile, corresponding examples were given to verify the feasibility of the proposed method.
文章引用:石孝安, 傅丽, 冶忠林, 王伟杰. 基于多粒度的多源决策信息系统的最优选择[J]. 交叉科学快报, 2025, 9(6): 909-918. https://doi.org/10.12677/isl.2025.96115

参考文献

[1] Pawlak, Z. (1982) Rough Sets. International Journal of Computer & Information Sciences, 11, 341-356. [Google Scholar] [CrossRef
[2] 张文修, 吴伟志, 梁吉业, 等.粗糙集理论与方法[M]. 北京: 科学出版社, 2001.
[3] 张文修, 仇国芳. 基于粗糙集的不确定决策[M]. 北京: 清华大学出版社, 2005.
[4] Lin, G.P., Liang, J.Y. and Qian, Y.H. (2008) An Information Fusion Approach by Combining Multi-Granulation Rough Sets and Evidence Theory. Information Sciences, 49, 466-477.
[5] Zhang, X.Y., Huang, X.D. and Xu, W.H. (2023) Matrix-Based Multi-Granulation Fusion Approach for Dynamic Updating of Knowledge in Multi-Source Information. Knowledge-Based Systems, 262, Article 110257.
[6] Zhang, X.Y., Chen, X.W., Xu, W.H., et al. (2022) Dynamic Information Fusion in Multi-Source Incomplete Interval-Valued Information System with Variation of Information Sources and Attributes. Information Sciences, 608, 1-27.
[7] 陈辉皇. 多源信息系统中的决策规则挖掘研究[D]: [硕士学位论文]. 漳州: 闽南师范大学, 2018.
[8] 7Xu, W., Li, M. and Wang, X. (2017) Information Fusion Based on Information Entropy in Fuzzy Multi-Source Incomplete Information System. International Journal of Fuzzy Systems, 19, 1200-1216. [Google Scholar] [CrossRef
[9] Yang, R., Li, H. and Huang, H. (2024) Multisource Information Fusion Considering the Weight of Focal Element’s Beliefs: A Gaussian Kernel Similarity Approach. Measurement Science and Technology, 35, Article 025136. [Google Scholar] [CrossRef
[10] Liu, N., Hu, J. and Liang, W. (2023) MIFINN: A Novel Multi-Information Fusion and Interaction Neural Network for Aspect-Based Sentiment Analysis. Knowledge-Based Systems, 280, Article 110983. [Google Scholar] [CrossRef
[11] Zhang, M., Cui, H., Tian, X., Kang, B. and Huang, L. (2023) Evaluate the Reliability of Information Sources Using the Non-Parametric Plausibility ReliefF Algorithm for Multi-Source Information Fusion. Applied Soft Computing, 148, Article 110871. [Google Scholar] [CrossRef
[12] 万青, 马盈仓, 魏玲. 基于多粒度的多源数据知识获取[J]. 山东大学学报(理学版), 2020, 55(1): 41-50.
[13] Wu, W.Z.,Chen, Y., Xu, Y.H., et al. (2016) Optimal Granularity Selsctions in Consistent Incomplete Multi-Granularlabeled Decision Systems. Pattern Recognition and Artificial Intelligence, 29, 108-115.
[14] Wu, W. and Leung, Y. (2011) Theory and Applications of Granular Labelled Partitions in Multi-Scale Decision Tables. Information Sciences, 181, 3878-3897. [Google Scholar] [CrossRef
[15] Wu, W. and Leung, Y. (2013) Optimal Scale Selection for Multi-Scale Decision Tables. International Journal of Approximate Reasoning, 54, 1107-1129. [Google Scholar] [CrossRef
[16] 刘凤玲, 林国平, 余晓龙. 多决策的多粒度标记系统的最优粒度选择[J]. 重庆理工大学学报(自然科学), 2020, 34(5): 263-270.
[17] 顾沈明, 顾金燕, 吴伟志, 等. 不完备多粒度决策系统的局部最优粒度选择[J]. 计算机研究与发展, 2017, 54(7): 1500-1509.
[18] Li, F. and Hu, B.Q. (2017) A New Approach of Optimal Scale Selection to Multi-Scale Decision Tables. Information Sciences, 381, 193-208. [Google Scholar] [CrossRef
[19] 吴伟志, 杨丽, 谭安辉, 等. 广义不完备多粒度标记决策系统的粒度选择[J]. 计算机研究与发展, 2018, 5(6): 1263-1272.
[20] 史进玲, 张倩倩, 徐久成. 多粒度决策系统属性约简的最优粒度选择[J]. 计算机科学, 2018, 45(2): 152-156.
[21] 吴伟志. 多粒度标记数据的知识表示和知识获取研究[Z]. 浙江海洋学院, 2013-12-30.
[22] Zhao, H., Mi, J. and Liang, M. (2022) A Multi-Granularity Information Fusion Method Based on Logistic Regression Model and Dempster-Shafer Evidence Theory and Its Application. International Journal of Machine Learning and Cybernetics, 13, 3131-3142. [Google Scholar] [CrossRef
[23] Yu, Y., Tang, H., Qian, J., Zhu, Z., Cai, Z. and Lv, J. (2022) Fine-grained Image Recognition via Trusted Multi-Granularity Information Fusion. International Journal of Machine Learning and Cybernetics, 14, 1105-1117. [Google Scholar] [CrossRef
[24] Deng, J., Liu, H., Fang, H., Shao, S., Wang, D., Hou, Y., et al. (2023) MgNet: A Fault Diagnosis Approach for Multi-Bearing System Based on Auxiliary Bearing and Multi-Granularity Information Fusion. Mechanical Systems and Signal Processing, 193, Article 110253. [Google Scholar] [CrossRef