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Attribute Reduction Based on Minimum Cover of Graph Vertices in Ordered Decision System

DOI: 10.12677/HJDM.2023.134032, PP. 327-334

Keywords: 粗糙集,序决策系统,图顶点最小覆盖理论,属性约简
Rough Set
, Order Decision System, Graph Vertex Minimum Covering Theory, Attribute Reduction

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In today’s Internet era, the data dimension has grown more dramatically, and how to extract useful information from high-dimensional data has become a big problem. Attribute reduction is one of the important steps of data preprocessing, which can reduce the attribute dimension and computa-tional complexity, and improve the classification performance and interpretability. Traditional at-tribute reduction methods are mainly based on information theory, statistics or heuristic algorithms, which are shortcomings. In this paper, we propose a method based on the minimum cover-age of graph vertices to model the dependencies between properties and combining the attribute reduction algorithm and graph theory knowledge. Experimental results show that the present method has good reduction and classification performance on multiple datasets, with good inter-pretability and visualization.


[1]  Pawlak, Z. (1982) Rough Sets. International Journal of Computer and Information Sciences, 11, 341-356.
[2]  Chen, Q., Huang, M., Wang, H. and Xu, G. (2022) A Feature Discreti-zation Method Based on Fuzzy Rough Sets for High-Resolution Remote Sensing Big Data under Linear Spectral Model. IEEE Transactions on Fuzzy Systems, 30, 1328-1342.
[3]  Dai, J.H., Hu, H., Zheng, G.J., et al. (2016) Attribute Reduction in Interval-Valued Information Systems Based on Infor-mation Entropies. Frontiers of Information Technology & Electronic Engineering, 17, 919-928.
[4]  Sun, L., Yin, T., Ding, W., Qian, Y. and Xu, J. (2022) Feature Selec-tion with Missing Labels Using Multilabel Fuzzy Neighborhood Rough Sets and Maximum Relevance Minimum Re-dundancy. IEEE Transactions on Fuzzy Systems, 30, 1197-1211.
[5]  Bao, H., Wu, W.Z., Zheng, J.W. and Li, T.J. (2021) Entropy Based Optimal Scale Combination Selection for Generalized Multi-Scale Information Tables. International Journal of Machine Learning and Cybernetics, 12, 1427-1437.
[6]  Yang, X.B., Qi, Y., Yu, D.J., Yu, H.L. and Yang, J.Y. (2015) α-Dominance Relation and Rough Sets in Interval-Valued Information Systems. Information Sciences, 294, 334-347.
[7]  Qian, Y.H., Liang, X.Y., Wang, Q., Liang, J.Y., Liu, B., Skowron, A., Yao, Y.Y., Ma, J.M. and Dang, C.Y. (2018) Local Rough Set: A Solution to Rough Data Analysis in Big Data. In-ternational Journal of Approximate Reasoning, 97, 38-63.
[8]  Shu, W.H. and Qian, W.B. (2015) An Incremental Approach to Attribute Reduction from Dynamic Incomplete Decision Sys-tems in Rough Set Theory. Data & Knowledge Engineering, 100, 116-132.
[9]  Yao, Y.Y. and Zhang, X.Y. (2017) Class-Specific Attribute Re-ducts in Rough Set Theory. Information Sciences, 418-419, 601-618.
[10]  张楠, 苗夺谦, 岳晓冬. 区间值信息系统的知识约简[J]. 计算机研究与发展, 2010, 47(8): 1362-1371.
[11]  Skowron, A. and Rauszer, C. (1992) The Discernibility Matrices and Func-tions in Information Systems. In: S?owiński, R., Ed., Intelligent Decision Support, Springer, Dordrecht, 331-362.
[12]  Huang, Z.H., Li, J.J., Dai, W.Z. and Lin, R. (2019) General-ized Multi-Scale Decision Tables with Multi-Scale Decision Attributes. International Journal of Approximate Reasoning, 115, 194-208.
[13]  Huang, Y., Li, T., Luo, C., et al. (2017) Matrix-Based Dynamic Updating Rough Fuzzy Approximations for Data Mining. Knowledge-Based Systems, 119, 273-283.
[14]  孙祖文, 唐玉凯. 序决策系统下近似约简的启发式算法[J]. 计算机科学与应用, 2021, 11(1): 113-120.
[15]  Bretto, A. (2013) Hypergraph Theory. Springer, Cham.


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