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《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (5): 82-89.doi: 10.6040/j.issn.1671-9352.7.2023.384

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决策集值系统中的知识约简

方逢祺1,吴伟志1,2*   

  1. 1. 浙江海洋大学信息工程学院, 浙江 舟山 316022;2.浙江省海洋大数据挖掘与应用重点实验室(浙江海洋大学), 浙江 舟山 316022
  • 发布日期:2024-05-09
  • 通讯作者: 吴伟志(1964— ),男,教授,博士生导师,博士,研究方向为粗糙集理论、粒计算、概念格、近似推理等.E-mail: wuwz@zjou.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(12371466,61976194,62076221)

Knowledge reduction in decision set-valued systems

FANG Fengqi1, WU Weizhi1,2*   

  1. 1. School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China;
    2. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province(Zhejiang Ocean University), Zhoushan 316022, Zhejiang, China
  • Published:2024-05-09

摘要: 针对决策为集合值的数据集的知识约简问题,定义了决策集值系统、确定性决策集值系统和倾向性决策集值系统等几类决策系统的概念。对比了决策集值系统与相类似的几类信息系统的区别,明确了决策集值系统的相关特点。结合三支决策方法,定义了决策集值系统上的单值约简与多值约简的概念,并给出了在确定性决策集值系统上计算约简的方法。结果表明,该方法在确定性决策集值系统上能有效提取信息。

关键词: 粒计算, 信息系统, 决策集值系统, 三支决策

Abstract: To solve the problem of knowledge reduction in data sets with a set-valued decision, several types of decision systems such as decision set-valued systems, certainty decision set-valued systems and propensity decision set-valued systems are first defined. A comparative study is then discussed on decision set-valued systems and several relevant types of information systems, and characteristics of decision set-valued systems are clarified. Finally, combined with the three-way decision method, notions of single-valued reducts and multi-valued reducts in decision set-valued systems are proposed and a method for the computation of reducts in decision set-valued systems is explored. The results show that the method can effectively extract information on certainty decision set-valued systems.

Key words: granular computing, information systems, decision set-valued systems, three-way decision

中图分类号: 

  • TP18
[1] ZADEH L A. Fuzzy sets and information granularity[M]. Amsterdam: North-Holland, 1979:3-18.
[2] LIN Tsauyoung. Granular computing on binary relations I: data mining and neighborhood system[M]. Heidelberg: Physica-Verlag, 1998:107-121.
[3] LIN Tsauyoung. Granular computing: structures, representations, and applications[C] //Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Berlin: Springer, 2003:16-24.
[4] YAO Yiyu. Granular computing: basic issues and possible solutions[C] //Proceedings of the 5th Joint Conference on Information Sciences. Durham: Duke University Press, 2000:186-189.
[5] 苗夺谦,王国胤,刘清,等. 粒计算:过去、现在与展望[M]. 北京:科学出版社, 2007. MIAO Duoqian, WANG Guoyin, LIU Qing, et al. Granular computing: past, present and future[M]. Beijing: Science Press, 2007.
[6] 苗夺谦,李德毅,姚一豫,等. 不确定性与粒计算[M]. 北京:科学出版社, 2011. MIAO Duoqian, LI Deyi, YAO Yiyu, et al. Uncertainty and granular computing[M]. Beijing: Science Press, 2011.
[7] PAWLAK Z. Rough sets: theoretical aspects of reasoning about data[M]. Dordrecht: Kluwer Academic Publishers, 1991.
[8] INUIGUCHI M, HIRANO S, TSUMOTO S. Rough set theory and granular computing[M]. Berlin: Springer, 2003.
[9] LIN Tsauyoung, YAO Yiyu, ZADEH L A. Data mining, rough sets and granular computing[M]. Heidelberg: Physica-Verlag, 2002.
[10] 徐伟华,米据生,吴伟志. 基于包含度的粒计算方法与应用[M]. 北京:科学出版社, 2015. XU Weihua, MI Jusheng, WU Weizhi. Granular computing methods and applications based on inclusion degree[M]. Beijing: Science Press, 2015.
[11] PEDRYCZ W, SKOWRON A, KREINOVICH V. Handbook of granular computing[M]. New York: Wiley, 2008.
[12] 张文修,梁怡,吴伟志.信息系统与知识发现[M]. 北京:科学出版社, 2003. ZHANG Wenxiu, LIANG Yi, WU Weizhi. Information systems and knowledge discovery[M]. Beijing: Science Press, 2003.
[13] 段洁,胡清华,张灵均,等.基于邻域粗糙集的多标记分类特征选择算法[J].计算机研究与发展,2015,52(1):56-65. DUAN Jie, HU Qinghua, ZHANG Lingjun, et al. Feature selection for multi-label classification based on neighborhood rough sets[J]. Journal of Computer Research and Development, 2015, 52(1):56-65.
[14] PAWLAK Z, SOWINSKI R. Rough set approach to multi-attribute decision analysis[J]. European Journal of Operational Research, 1994, 72(3):443-459.
[15] GRECO S, MATARAZZO B, SLOWINSKI R. Rough sets methodology for sorting problems in presence of multiple attributes and criteria[J]. European Journal of Operational Research, 2002, 138(2):247-259.
[16] MENDONCA G H M, FERREIRA F G D C, CARDOSO R T N, et al. Multi-attribute decision making applied to financial portfolio optimization problem[J]. Expert Systems with Applications, 2020, 158:113527.
[17] NIU Jiaojiao, CHEN Degang, LI Jinhai, et al. A dynamic rule-based classification model via granular computing[J]. Information Sciences, 2022, 584:325-341.
[18] XIE Xiaojun, QIN Xiaolin. A novel incremental attribute reduction approach for dynamic incomplete decision systems[J]. International Journal of Approximate Reasoning, 2018, 93:443-462.
[19] YANG Xin, LIU Dun, YANG Xibei, et al. Incremental fuzzy probability decision-theoretic approaches to dynamic three-way approximations[J]. Information Sciences, 2021, 550:71-90.
[20] HUANG Yanyong, LI Tianrui, LUO Chuan, et al. Matrix-based dynamic updating rough fuzzy approximations for data mining[J]. Knowledge-based Systems, 2017, 119:273-283.
[21] ZHANG Junbo, LI Tianrui, RUAN Da, et al. Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems[J]. International Journal of Approximate Reasoning, 2012, 53(4):620-635.
[22] GU Shenming, WU Weizhi. On knowledge acquisition in multi-scale decision systems[J]. International Journal of Machine Learning and Cybernetics, 2013, 4:477-486.
[23] 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.
[24] WU Weizhi, QIAN Yuhua, LI Tongjun, et al. On rule acquisition in incomplete multi-scale decision tables[J]. Information Sciences, 2017, 378:282-302.
[25] XIE Junping, YANG Minhua, LI Jinhai, et al. Rule acquisition and optimal scale selection in multi-scale formal decision contexts and their applications to smart city[J]. Future Generation Computer Systems, 2018, 83:564-581.
[26] HAO Chen, LI Jinhai, FAN Min, et al. Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions[J]. Information Sciences, 2017, 415/416:213-232.
[27] AZAM N, ZHANG Yan, YAO Jingtao. Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets[J]. European Journal of Operational Research, 2017, 261:704-714.
[28] CABITZA F, CIUCCI D, LOCORO A. Exploiting collective knowledge with three-way decision theory:cases from the questionnaire-based research[J]. International Journal of Approximate Reasoning, 2017, 83:356-370.
[29] HU Baoqing. Three-way decisions based on semi-three-way decision spaces[J]. Information Sciences, 2017, 382/383:415-440.
[30] HU Mengjun, YAO Yiyu. Structured approximations as a basis for three-way decisions in rough set theory[J]. Knowledge-based Systems, 2019, 165:92-109.
[31] YANG Xin, LI Tianrui, LIU Dun, et al. A temporal-spatial composite sequential approach of three-way granular computing[J]. Information Sciences, 2019, 486:171-189.
[32] LANG Guangming, MIAO Duoqian, FUJITA H. Three-way group conflict analysis based on Pythagorean fuzzy set theory[J]. IEEE Transactions on Fuzzy Systems, 2020, 28(3):447-461.
[33] YU Hong, WANG Xincheng, WANG Guoyin, et al. An active three-way clustering method via low-rank matrices for multi-view data[J]. Information Sciences, 2020, 507:823-839.
[34] YUE Xiaodong, CHEN Yufei, MIAO Duoqian, et al. Fuzzy neighborhood covering for three-way classification[J]. Information Sciences, 2020, 507:795-808.
[35] ZHAO Xuerong, HU Baoqing. Three-way decisions with decision-theoretic rough sets in multiset-valued information tables[J]. Information Sciences, 2020, 507:684-699.
[36] ZHAO Yan, YAO Yiyu, LUO Feng. Data analysis based on discernibility and indiscernibility[J]. Information Sciences, 2007, 177:4959-4976.
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