JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (5): 82-89.doi: 10.6040/j.issn.1671-9352.7.2023.384

Previous Articles     Next Articles

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

RichHTML

0

PDF (PC)

171

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

CLC Number: 

  • 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.
[1] FAN Min, QIN Qin, LI Jinhai. Neighborhood recommendation algorithm based on three-way causality force [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(5): 12-22.
[2] ZHU Jin, FU Yu, GUAN Wenrui, WANG Pingxin. Perturbation three-way clustering based on natural nearest neighbors [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(5): 45-51.
[3] ZHENG Chenying, CHEN Yingyue, HOU Xianyu, JIANG Lianji, LIAO Liang. A neighbourhood granular fuzzy C-means clustering algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(5): 35-44.
[4] CHEN Yumin, ZHENG Guangyu, JIAO Na. Multi-label learning based on granular neural networks [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(5): 1-11.
[5] Qian WANG,Xianyong ZHANG. Incomplete neighborhood weighted multi-granularity decision-theoretic rough sets and three-way decision [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(9): 94-104.
[6] Junyu WANG,Yafeng YANG,Jingxuan XUE,Lihong LI. Extension sequential three-way decision model and its application [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(7): 67-79.
[7] Yu FANG,Huyu ZHENG,Xuemei CAO. Three-way over-sampling method for imbalanced data classification [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(12): 41-51.
[8] FAN Jia-chen, WANG Ping-xin, YANG Xi-bei. Density-sensitive spectral clustering based on three-way decision [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(1): 59-66.
[9] QIAN Jin, TANG Da-wei, HONG Cheng-xin. Research on multi-granularity hierarchical sequential three-way decision model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2022, 57(9): 33-45.
[10] GONG Zeng-tai, TA Guang-peng. Semantics of the soft set induced by intuitionistic fuzzy set and its three-way decision [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2022, 57(8): 68-76.
[11] SHI Ji, SUO Zhong-ying. Loss function determination method based on interval number analytic hierarchy process [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2022, 57(5): 28-37.
[12] YANG Jie, LUO Tian, LI Yang-jun. Unlabeled sequential three-way decisions model based on TOPSIS [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2022, 57(3): 41-48.
[13] LI Min, YANG Ya-feng, LEI Yu, LI Li-hong. Optimal granularity selection based on minimum cost of extension domain change [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2021, 56(2): 17-27.
[14] Jie TANG,Ling WEI,Rui-si REN,Si-yu ZHAO. Granule description using possible attribute analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2021, 56(1): 75-82.
[15] LI Jin-hai, HE Jian-jun, WU Wei-zhi. Optimization of class-attribute block in multi-granularity formal concept analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2020, 55(5): 1-12.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] YANG Ying, JIANG Long*, SUO Xin-li. Choquet integral representation of premium functional and related properties on capacity space[J]. J4, 2013, 48(1): 78 -82 .
[2] XIE Yun-long,DU Ying-ling . Function S-rough sets and integral metric of laws[J]. J4, 2007, 42(10): 118 -122 .
[3] LIANG Xiao, WANG Linshan. Global attractor of a class of recurrent neural network with Stype distributed delays[J]. J4, 2009, 44(4): 57 -60 .
[4] DONG Xin-mei . On problems of Suryanarayana[J]. J4, 2007, 42(2): 83 -86 .
[5] LI Zhi-Chao, FU Gong-Fei. The dynamic finite element method with characteristics for convectiondominated diffusion problems[J]. J4, 2009, 44(8): 90 -96 .
[6] XU Chun-hua,GAO Bao-yu,LU Lei,XU Shi-ping,CAO Bai-chuan,YUE Qin-yan and ZHANG Jian . Study of chemically enhanced primary treatment of wastewater received by urban rivers[J]. J4, 2006, 41(2): 116 -120 .
[7] CHEN Hong-yu1, ZHANG Li2. The linear 2-arboricity of planar graphs without 5-, 6-cycles with chord[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(06): 26 -30 .
[8] HUANG Xian-li，LUO Dong-mei. Feature impprtance study on  transfer learning of  sentiment  text  classification[J]. J4, 2010, 45(7): 13 -17 .
[9] GAO Zheng-hui, LUO Li-ping. Philos-type oscillation criteria for third-order nonlinear functional differential equations with distributed delays and damped terms[J]. J4, 2013, 48(4): 85 -90 .
[10] LI Zhi-rong . Computational formulae of generalized m-th-order Bell numbers and generalized m-order orderd Bell numbers[J]. J4, 2007, 42(2): 59 -63 .
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd