基于最大决策熵的快速属性约简算法
Fast Attribute Reduction Algorithm Based on Maximum Decision Entropy
DOI: 10.12677/HJDM.2023.133022, PDF,    科研立项经费支持
作者: 袁 梅：烟台大学计算机与控制工程学院，山东 烟台
关键词: 快速属性约简算法；粗糙集；最大决策熵；决策系统；Fast Attribute Reduction Algorithm； Rough Set； Maximum Decision Entropy； Decision System
摘要: 在大数据时代背景下，各领域数据爆炸式增长，数据类型复杂多样。针对决策系统中基于最大决策熵的属性约简算法在大规模数据集下运行效率低的问题，提出了一种基于启发式的快速属性约简算法。本文提出的算法首先研究了属性和对象在属性约简过程中的变化对其产生影响，其次提出了属性重要度保序性的相关定理。最后通过UCI数据集对提出算法的有效性进行验证，结果表明提出的快速属性约简算法的运行效率更高。
Abstract: In the era of big data, data in various fields is growing explosively, and data types are complex and diverse. Aiming at the low efficiency of attribute reduction algorithm based on maximum decision entropy in decision system under large data sets, a fast attribute reduction algorithm based on heuristic is proposed. The algorithm proposed in this paper firstly studies the influence of the changes of attributes and objects in the process of attribute reduction, and then puts forward the related theorem about the rank preservation of attributes. Finally, the effectiveness of the proposed algorithm is verified by the UCI data set, and the results show that the proposed fast attribute re-duction algorithm is more efficient.
文章引用：袁梅. 基于最大决策熵的快速属性约简算法[J]. 数据挖掘, 2023, 13(3): 222-229. https://doi.org/10.12677/HJDM.2023.133022

参考文献

[1] Pawlak, Z. (1982) Rough Sets. International Journal of Computer and Information Sciences, 11, 341-356. [Google Scholar] [CrossRef] 
[2] 杨习贝, 颜旭, 徐苏平, 于化龙. 基于样本选择的启发式属性约简方法研究[J]. 计算机科学, 2016, 43(1): 40-43.
[3] Chen, H.M., Li, T.R., Cai, Y., Luo, C. and Fujita, H. (2016) Par-allel Attribute Reduction in Dominance-Based Neighborhood Rough Set. Information Sciences, 373, 351-368. [Google Scholar] [CrossRef] 
[4] Wang, C.Z., Shao, M.W., Sun, B.Q. and Hu, Q.H. (2015) An Im-proved Attribute Reduction Scheme with Covering Based Rough Sets. Applied Soft Computing, 26, 235-243. [Google Scholar] [CrossRef] 
[5] Min, F., Zhang, Z.H. and Dong, J. (2018) Ant Colony Optimiza-tion with Partial-Complete Searching for Attribute Reduction. Journal of Computational Science, 25, 170-182. [Google Scholar] [CrossRef] 
[6] Miao, D.Q., Zhao, Y., Yao, Y.Y., Li, H.X. and Xu, F.F. (2009) Relative Reducts in Consistent and Inconsistent Decision Tables of the Pawlak Rough Set Model. Information Sciences, 179, 4140-4150. [Google Scholar] [CrossRef] 
[7] Kryszkiewicz, M. (1998) Rough Set Approach to Incomplete Infor-mation Systems. Information Sciences, 112, 39-49. [Google Scholar] [CrossRef] 
[8] 王国胤, 于洪, 杨大春. 基于条件信息熵的决策表约简[J]. 计算机学报, 2002, 25(7): 759-766.
[9] Gao, C., Lai, Z.H., Zhou, J., Zhao, C.R. and Miao, D.Q. (2108) Maxi-mum Decision Entropy-Based Attribute Reduction in Decision-Theoretic Rough Set Model. Knowledge-Based Systems, 143, 179-191. [Google Scholar] [CrossRef] 
[10] Zhang, N., Gao, X.Y. and Yu, T.Y. (2019) Heuristic Ap-proaches to Attribute Reduction for Generalized Decision Preservation. Applied Sciences, 9, Article 2841. [Google Scholar] [CrossRef] 
[11] 徐章艳, 刘作鹏, 杨炳儒, 宋威. 一个复杂度为 的快速属性约简算法[J]. 计算机学报, 2006, 29(3): 391-399.
[12] Qian, Y.H., Liang, J.Y., Pedrycz, W. and Dang, C.Y. (2010) Positive Approximation: An Accelerator for Attribute Reduction in Rough Set Theory. Artificial Intelligence, 174, 597-618. [Google Scholar] [CrossRef] 
[13] Du, W.S. and Hu, B.Q. (2018) A Fast Heuristic Attribute Reduction Approach to Ordered Decision Systems. European Journal of Operational Research, 264, 440-452. [Google Scholar] [CrossRef] 
[14] Sang, B.B., Chen, H.M., Yang, L., Zhou, D.P., Li, T.R. and Xu, W.H. (2021) Incremental Attribute Reduction Approaches for Ordered Data with Time-Evolving Objects. Knowledge-Based Systems, 212, Article ID: 106583. [Google Scholar] [CrossRef] 
[15] Dong, L.J. and Chen, D.G. (2020) Incremental Attribute Re-duction with Rough Set for Dynamic Datasets with Simultaneously Increasing Samples and Attributes. International Journal of Machine Learning and Cybernetics, 11, 1339-1355. [Google Scholar] [CrossRef] 
[16] Shu, W.H., Qian, W.B. and Xie, Y.H. (2020) Incremental Fea-ture Selection for Dynamic Hybrid Data Using Neighborhood Rough Set. Knowledge-Based Systems, 194, Article ID: 105516. [Google Scholar] [CrossRef] 
[17] 鲍迪, 张楠, 童向荣, 岳晓东. 区间值决策表的正域增量式属性约简算法[J]. 计算机应用, 2019, 39(8): 2288-2296.

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