JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2022, Vol. 57 ›› Issue (6): 74-83.doi: 10.6040/j.issn.1671-9352.0.2021.691

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Optimal scale reduction based on graph theory in consistent multi-scale decision tables

JIN Ming1, CHEN Jin-kun1,2*   

  1. 1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, Fujian, China;
    2. Fujian Key Laboratory of Granular Computing and Applications, Minan Normal University, Zhangzhou 363000, Fujian, China
  • Published:2022-06-10

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Abstract: Multi-scale decision table is a model based on real-world data with multi-scale background. It is a difficult problem that how to carry out optimal scale reduction of multi-scale decision tables. By constructing the identification matrix on multi-scale, the properties of the identification matrix are put forward, and the relationship between the matrix and the optimal scale reduction is given. A fast algorithm for optimal scale reduction is presented by combining the identification matrix with graph theory. Finally, the effectiveness of the proposed algorithm is verified via numerical experiments.

Key words: consistent multi-scale decision table, decision identification matrice, optimal scale reduction, graph theory

CLC Number: 

  • TP18
[1] JIA Xiuyi, SHANG Lin, ZHOU Bing, et al. Generalized attribute reduct in rough set theory[J]. Knowledge-Based Systems, 2016, 91:204-218.
[2] LEUNG Yee, LI Deyu. Maximal consistent block technique for rule acquisition in incomplete information systems[J]. Information Sciences, 2003, 153:85-106.
[3] LEUNG Yee, ZHANG Jiangshe, XU Zongben. Clustering by scale-space filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12):1396-1410.
[4] WU Weizhi, LEUNG Yee. Theory and applications of granular labelled partitions in multi-scale decision tables[J]. Information Sciences, 2011, 181(18):3878-3897.
[5] GU Shenming, WU Weizhi. On knowledge acquisition in multi-scale decision systems[J]. International Journal of Machine Learning and Cybernetics, 2013, 4(5):477-486.
[6] WU Weizhi, LEUNG Yee. Optimal scale selection for multi-scale decision tables[J]. International Journal of Approximate Reasoning, 2013, 54(8):1107-1129.
[7] 魏巍,张嘉宇,陈千,等.基于熵的多尺度决策表最优尺度选择[J]. 海南热带海洋学院学报, 2018, 25(5):61-65. WEI Wei, ZHANG Jiayu, CHEN Qian, et al. Entropy-based optimal scale selection in multi-scale decision tables[J]. Journal of Hainan Tropical Ocean University, 2018, 25(5):61-65.
[8] LI Feng, HU Baoqing. A new approach of optimal scale selection to multi-scale decision tables[J]. Information Sciences, 2017, 381:193-208.
[9] SHE Yanhong, LI Jinhai, YANG Hailong. A local approach to rule induction in multi-scale decision tables[J]. Knowledge-Based Systems, 2015, 89:398-410.
[10] WU Weizhi, LEUNG Yee. A comparison study of optimal scale combination selection in generalized multi-scale decision tables[J]. International Journal of Machine Learning and Cybernetics, 2020, 11(5):961-972.
[11] 陈应生,李进金,林荣德,等.多尺度覆盖决策信息系统的布尔矩阵方法[J]. 模式识别与人工智能, 2020, 33(9):776-785. CHEN Yingshe, LI Jinjin, LIN Rongde, et al. Boolean matrix approach for multi-scale covering decision information systems[J]. Pattern Recognition and Artificial Intelligence, 2020, 33(9):776-785.
[12] LI Feng, HU Baoqing, WANG Jun. Stepwise optimal scale selection for multi-scale decision tables via attribute significance[J]. Knowledge-Based Systems, 2017, 129:4-16.
[13] 张清华,张雪秋,庞国弘.多尺度决策系统中代价敏感的最优尺度组合[J]. 控制与决策, 2021, 36(10):2369-2378. ZHANG Qinghua, ZHANG Xueqiu, PANG Guohong. Cost-sensitive optimal scale combination in multi-scale decision systems[J]. Control and Decision, 2021, 36(10):2369-2378.
[14] ZHANG Xueqiu, ZHANG Qinghua, CHENG Yunlong, et al. Optimal scale selection by integrating uncertainty and cost-sensitive learning in multi-scale decision tables[J]. International Journal of Machine Learning and Cybernetics, 2020, 11(1):1095-1114.
[15] WU Weizhi, QIAN Yuhua, LI Tongjun, et al. On rule acquisition in incomplete multi-scale decision tables[J]. Information Sciences, 2017, 378: 282-302.
[16] CHENG Yunlong, ZHANG Qinghua, WANG Guoyin, et al. Optimal scale selection and attribute reduction in multi-scale decision tables based on three-way decision[J]. Information Sciences, 2020, 541:36-59.
[17] SHE Yanhong, QIAN Zhuohao, HE Xiaoli, et al. On generalization reducts in multi-scale decision tables[J]. Information Sciences, 2020, 555:104-124.
[18] MIAO Duoqian, ZHAO Yan, YAO Yiyu, et al. Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model[J]. Information Sciences, 2009, 179(24):4140-4150.
[19] 米据生, 陈锦坤. 基于图的粗糙集属性约简方法[J]. 西北大学学报(自然科学版), 2019, 49(4):508-516. MI Junsheng, CHEN Jinkun. Graph-based approaches for attribute reduction in rough sets[J]. Journal of Northwest University(Natural Science Edition), 2019, 49(4):508-516.
[20] 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: 35-38. ZHANG Wenxiu, QIU Guofang. Uncertain decision making based on rough sets[M]. Beijing: Tsinghua University Press, 2005: 35-38.
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