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| Review on Multi-granulation Computing Models and Methods for Decision Analysis |
| PANG Jifang1, SONG Peng2, LIANG Jiye1,3 |
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006;
2. School of Economics and Management, Shanxi University, Taiyuan 030006;
3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006 |
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Abstract As the core concept and key technology of granular computing, multi-granulation computing emphasizes multi-view and multi-level understanding and description of real-world problems to obtain more reasonable and satisfactory results. The existing four types of multi-granulation computing models are firstly introduced, including multi-granulation rough set, multi-scale data analysis, sequential three-way decision and hierarchical classification learning, for the further effective fusion of multi-granulation computing and decision analysis and better satisfaction with actual decision-making needs. Then, their main characteristics and development process are expounded. Furthermore, the research status of decision analysis methods based on multi-granulation computing models is summarized from the aspects of attribute reduction, rule extraction, granularity selection, information fusion, group decision-making, multi-attribute group decision-making, classification decision-making and dynamic decision-making. Finally, some challenging research directions of intelligent decision-making in the era of big data are forecasted to promote the continuous development and innovation of multi-granulation intelligent decision-making.
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Received: 07 May 2021
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| Fund:National Natural Science Foundation of China(No.62006148), Key Research and Development Plan of Shanxi Province(No.201903D121162) |
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Corresponding Authors:
LIANG Jiye, Ph.D., professor. His research interests include artificial intelligence, granular computing, data mining and machine learning.
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About author:: PANG Jifang, Ph.D., associate professor. Her research interests include granular computing, intelligent decision and data mi-ning.
SONG Peng, Ph.D., professor. His research interests include intelligent decision and data mining. |
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[1] 于洪,何德牛,王国胤,等.大数据智能决策.自动化学报, 2020, 46(5): 878-896.
(YU H, HE D N, WANG G Y, et al. Big Data for Intelligent Decision Making. Acta Automatica Sinica, 2020, 46(5): 878-896.)
[2] PEDRYCZ W.Granular Computing for Data Analytics: A Manifesto of Human-Centric Computing. IEEE/CAA Journal of Automatica Sinica, 2018, 5(6): 1025-1034.
[3] 梁吉业,钱宇华,李德玉,等.大数据挖掘的粒计算理论与方法.中国科学(信息科学), 2015, 45(11): 1355-1369.
(LIANG J Y, QIAN Y H, LI D Y, et al. Theory and Method of Granular Computing for Big Data Mining. Scientia Sinica(Informationis), 2015, 45(11): 1355-1369.)
[4] QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: A Multi-granulation Rough Set. Information Sciences, 2010, 180(6): 949-970.
[5] WU W Z, LEUNG Y.Theory and Applications of Granular Labelled Partitions in Multi-scale Decision Tables. Information Sciences, 2011, 181(18): 3878-3897.
[6] YAO Y Y, DENG X F.Sequential Three-Way Decisions with Pro-babilistic Rough Sets // Proc of the 10th IEEE International Confe-rence on Cognitive Informatics and Cognitive Computing. Washington, USA: IEEE, 2011: 120-125.
[7] 胡清华,王煜,周玉灿,等.大规模分类任务的分层学习方法综述.中国科学(信息科学), 2018, 48(5): 487-500.
(HU Q H, WANG Y, ZHOU Y C, et al. Review on Hierarchical Learning Methods for Large-Scale Classification Task. Scientia Sinica(Informationis), 2018, 48(5): 487-500.)
[8] QIAN Y H, LI S Y, LIANG J Y, et al. Pessimistic Rough Set Based Decisions: A Multigranulation Fusion Strategy. Information Sciences, 2014, 264(20): 196-210.
[9] QIAN Y H, ZHANG H, SANG Y L, et al. Multigranulation Decision-Theoretic Rough Sets. International Journal of Approximate Reasoning, 2014, 55(1): 225-237.
[10] LI Z W, LIU X F, ZHANG G Q, et al. A Multi-granulation Decision-Theoretic Rough Set Method for Distributed fc-Decision Information Systems: An Application in Medical Diagnosis. Applied Soft Computing, 2017, 56: 233-244.
[11] ZHAN J M, SUN B Z, ALCANTUD J C R. Covering Based Multigranulation(I,T)-Fuzzy Rough Set Models and Applications in Multi-attribute Group Decision-Making. Information Sciences, 2019, 476: 290-318.
[12] FENG T, MI J S. Variable Precision Multigranulation Decision-Theoretic Fuzzy Rough Sets. Knowledge-Based Systems, 2016, 91: 93-101.
[13] JU H R, LI H X, YANG X B, et al. Cost-Sensitive Rough Set: A Multi-granulation Approach. Knowledge-Based Systems, 2017, 123: 137-153.
[14] SUN B Z, MA W M.Multigranulation Rough Set Theory over Two Universes. Journal of Intelligent and Fuzzy Systems(Applications in Engineering and Technology), 2015, 28(3): 1251-1269.
[15] LIN G P, QIAN Y H, LI J J.NMGRS: Neighborhood-Based Multigranulation Rough Sets. International Journal of Approximate Reasoning, 2012, 53(7): 1080-1093.
[16] ZHAN J M, ZHANG X H, YAO Y Y.Covering Based Multigranulation Fuzzy Rough Sets and Corresponding Applications. Artificial Intelligence Review, 2020, 53(3): 1093-1126.
[17] LIN G P, LIANG J Y, QIAN Y H, et al. A Fuzzy Multigranulation Decision-Theoretic Approach to Multi-source Fuzzy Information Systems. Knowledge-Based Systems, 2016, 91: 102-113.
[18] YANG L, ZHANG X Y, XU W H, et al. Multi-granulation Rough Sets and Uncertainty Measurement for Multi-source Fuzzy Information System. International Journal of Fuzzy Systems, 2019, 21(6): 1919-1937.
[19] SANG B B, YANG L, CHEN H M, et al. Generalized Multi-granulation Double-Quantitative Decision-Theoretic Rough Set of Multi-source Information System. International Journal of Approximate Reasoning, 2019, 115: 157-179.
[20] GUO Y T, TSANG E C C, XU W H, et al. Adaptive Weighted Generalized Multi-granulation Interval-Valued Decision-Theoretic Rough Sets. Knowledge-Based Systems, 2020, 187. DOI: 10.1016/j.knosys.2019.06.012.
[21] WU W Z, QIAN Y H, LI T J, et al. On Rule Acquisition in Incomplete Multi-scale Decision Tables. Information Sciences, 2017, 378: 282-302.
[22] LI F, HU B Q.A New Approach of Optimal Scale Selection to Multi-scale Decision Tables. Information Sciences, 2017, 381: 193-208.
[23] 吴伟志,杨丽,谭安辉,等.广义不完备多粒度标记决策系统的粒度选择.计算机研究与发展, 2018, 55(6): 1263-1272.
(WU W Z, YANG L, TAN A H, et al. Granularity Selections in Generalized Incomplete Multi-granular Labeled Decision Systems. Journal of Computer Research and Development, 2018, 55(6): 1263-1272.)
[24] HUANG B, WU W Z, YAN J J, et al. Inclusion Measure-Based Multi-granulation Decision-Theoretic Rough Sets in Multi-scale Intuitionistic Fuzzy Information Tables. Information Sciences, 2020, 507: 421-448.
[25] 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.模式识别与人工智能, 2020, 33(9): 776-785.
(CHEN Y S, LI J J, LIN R D, et al. Boolean Matrix Approach for Multi-scale Covering Decision Information System. Pattern Recognition and Artificial Intelligence, 2020, 33(9): 776-785.)
[26] HAO C, LI J H, FAN M, ,et al. Optimal Scale Selection in Dynamic Multi-scale Decision Tables Based on Sequential Three-Way Decisions. Information Sciences. Optimal Scale Selection in Dynamic Multi-scale Decision Tables Based on Sequential Three-Way Decisions. Information Sciences, 2017, 415/416: 213-232.
[27] FANG Y, GAO C, YAO Y Y.Granularity-Driven Sequential Three-Way Decisions: A Cost-Sensitive Approach to Classification. Information Sciences, 2020, 507: 644-664.
[28] YANG X, ZHANG Y Y, FUJITA H, et al. Local Temporal-Spatial Multi-granularity Learning for Sequential Three-Way Granular Computing. Information Sciences, 2020, 541: 75-97.
[29] QIAN J, LIU C H, YUE X D.Multigranulation Sequential Three-Way Decisions Based on Multiple Thresholds. International Journal of Approximate Reasoning, 2019, 105: 396-416.
[30] YANG X, LI T R, LIU D, et al. A Multilevel Neighborhood Sequential Decision Approach of Three-Way Granular Computing. Information Sciences, 2020, 538: 119-141.
[31] 刘盾,李天瑞,梁德翠,等.三支决策的时空性.智能系统学报, 2019, 14(1): 141-149.
(LIU D, LI T R, LIANG D C, et al. Temporality and Spatiality of Three-Way Decisions. CAAI Transactions on Intelligent Systems, 2019, 14(1): 141-149.)
[32] ZHAO H, HU Q H, ZHU P F, et al. A Recursive Regularization Based Feature Selection Framework for Hierarchical Classification. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(7): 2833-2846.
[33] WANG Y, HU Q H, ZHU P F, et al. Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification. IEEE Transactions on Fuzzy Systems, 2020, 28(7): 1395-1406.
[34] 白盛兴,林耀进,王晨曦,等.基于邻域粗糙集的大规模层次分类在线流特征选择.模式识别与人工智能, 2019, 32(9): 811-820.
(BAI S X, LIN Y J, WANG C X, et al. Large-Scale Hierarchical Classification Online Streaming Feature Selection Based on Neighborhood Rough Set. Pattern Recognition and Artificial Intelligence, 2019, 32(9): 811-820.)
[35] LIANG J Y, WANG F, DANG C Y, et al. An Efficient Rough Feature Selection Algorithm with a Multi-granulation View. International Journal of Approximate Reasoning, 2012, 53(6): 912-926.
[36] LIANG J Y, WANG F, DANG C Y, et al. A Group Incremental Approach to Feature Selection Applying Rough Set Technique. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 294-308.
[37] GU S M, WU W Z.On Knowledge Acquisition in Multi-scale Decision Systems. International Journal of Machine Learning and Cybernetics, 2013, 4(5): 477-486.
[38] QIAN J, DANG C Y, YUE X D, et al. Attribute Reduction for Sequential Three-Way Decisions under Dynamic Granulation. International Journal of Approximate Reasoning, 2017, 85: 196-216.
[39] KONG Q Z, ZHANG X W, XU W H, et al. Attribute Reducts of Multi-granulation Information System. Artificial Intelligence Review, 2020, 53: 1353-1371.
[40] SUN L, WANG L Y, DING W P, et al. Neighborhood Multi-gra-nulation Rough Sets-Based Attribute Reduction Using Lebesgue and Entropy Measures in Incomplete Neighborhood Decision Systems. Knowledge-Based Systems, 2020, 192. DOI: 10.1016/j.knosys.2019.105373.
[41] SHE Y H, QIAN Z H, HE X L, et al. On Generalization Reducts in Multi-scale Decision Tables. Information Sciences, 2021, 555: 104-124.
[42] 陈静雯,马福民,张腾飞,等.基于最大粒的悲观邻域多粒度粗糙集规则获取算法.模式识别与人工智能, 2017, 30(11): 1048-1056.
(CHEN J W, MA F M, ZHANG T F, et al. Rule Acquisition Algorithm for Neighborhood Multi-granularity Rough Sets Based on Maximal Granule. Pattern Recognition and Artificial Intelligence, 2017, 30(11): 1048-1056.)
[43] FENG Q R, MIAO D Q, CHENG Y.Hierarchical Decision Rules Mining. Expert Systems with Applications, 2010, 37(3): 2081-2091.
[44] SHE Y H, LI J H, YANG H L.A Local Approach to Rule Induction in Multi-scale Decision Tables. Knowledge-Based Systems, 2015, 89: 398-410.
[45] LI J H, REN Y, MEI C L, et al. A Comparative Study of Multigranulation Rough Sets and Concept Lattices via Rule Acquisition. Knowledge-Based Systems, 2016, 91: 152-164.
[46] SINGH P, HUANG Y P.A Four-Way Decision-Making Approach Using Interval-Valued Fuzzy Sets, Rough Set and Granular Computing: A New Approach in Data Classification and Decision-Making. Granular Computing, 2020, 5(1): 397-409.
[47] WU W Z, LEUNG Y.Optimal Scale Selection for Multi-scale Decision Tables. International Journal of Approximate Reasoning, 2013, 54(8): 1107-1129.
[48] 吴伟志,高仓健,李同军.序粒度标记结构及其粗糙近似.计算机研究与发展, 2014, 51(12): 2623-2632.
(WU W Z, GAO C J, LI T J.Ordered Granular Labeled Structures and Rough Approximations. Journal of Computer Research and Development, 2014, 51(12): 2623-2632.)
[49] 顾沈明,顾金燕,吴伟志,等.不完备多粒度决策系统的局部最优粒度选择.计算机研究与发展, 2017, 54(7): 1500-1509.
(GU S M, GU J Y, WU W Z, et al. Local Optimal Granularity Selections in Incomplete Multi-granular Decision Systems. Journal of Computer Research and Development, 2017, 54(7): 1500-1509.)
[50] 梁美社,米据生,侯成军,等.基于局部广义多粒度粗糙集的多标记最优粒度选择.模式识别与人工智能, 2019, 32(8): 718-725.
(LIANG M S, MI J S, HOU C J, et al. Optimal Granulation Selection for Multi-label Data Based on Local Generalized Multi-gra-nulation Rough Set. Pattern Recognition and Artificial Intelligence, 2019, 32(8): 718-725.)
[51] CHENG Y L, ZHANG Q H, WANG G Y.Optimal Scale Combination Selection for Multi-scale Decision Tables Based on Three-Way Decision. International Journal of Machine Learning and Cybernetics, 2020, 12: 281-301.
[52] TAN A H, WU W Z, SHI S W, et al. Granulation Selection and Decision Making with Multigranulation Rough Set over Two Universes. International Journal of Machine Learning and Cybernetics, 2019, 10: 2501-2513.
[53] HU B Q, WONG H, YIU K F C. The Aggregation of Multiple Three-Way Decision Spaces. Knowledge-Based Systems, 2016, 98: 241-249.
[54] HUANG C C, LI J H, MEI C L, et al. Three-Way Concept Lear-ning Based on Cognitive Operators: An Information Fusion Viewpoint. International Journal of Approximate Reasoning, 2017, 83: 218-242.
[55] YANG L, XU W H, ZHANG X Y, et al. Multi-granulation Me-thod for Information Fusion in Multi-source Decision Information System. International Journal of Approximate Reasoning, 2020, 122: 47-65.
[56] WEI W, LIANG J Y.Information Fusion in Rough Set Theory: An Overview. Information Fusion, 2019, 48: 107-118.
[57] ZHANG P F, LI T R, WANG G Y, et al. Multi-source Information Fusion Based on Rough Set Theory: A Review. Information Fusion, 2021, 68: 85-117.
[58] HAN W, SUN Y H, XIE H, et al. Hesitant Fuzzy Linguistic Group DEMATEL Method with Multi-granular Evaluation Scales. International Journal of Fuzzy Systems, 2018, 20(7): 2187-2201.
[59] MANDAL P, RANADIVE A S.Multi-granulation Interval-Valued Fuzzy Probabilistic Rough Sets and Their Corresponding Three-Way Decisions Based on Interval-Valued Fuzzy Preference Relations. Granular Computing, 2019, 4: 89-108.
[60] CHU X L, SUN B Z, HUANG Q C, et al. Preference Degree-Based Multi-granularity Sequential Three-Way Group Conflict Decisions Approach to the Integration of TCM and Western Medicine. Computers and Industrial Engineering, 2020, 143. DOI: 10.1016/j.cie.2020.106393.
[61] ZHAN J M, ZHANG K, WU W Z.An Investigation on Wu-Leung Multi-scale Information Systems and Multi-expert Group Decision-Making. Expert Systems with Applications, 2021, 170. DOI: 10.1016/j.eswa.2020.114542.
[62] ZHANG C, LI D Y, LIANG J Y.Interval-Valued Hesitant Fuzzy Multi-granularity Three-Way Decisions in Consensus Process with Applications to Multi-attribute Group Decision Making. Information Sciences, 2020, 511: 192-211.
[63] PANG J F, LIANG J Y, SONG P.An Adaptive Consensus Method for Multi-attribute Group Decision Making under Uncertain Linguistic Environment. Applied Soft Computing, 2017, 58: 339-353.
[64] PANG J F, GUAN X Q, LIANG J Y, et al. Multi-attribute Group Decision-Making Method Based on Multi-granulation Weights and Three-Way Decisions. International Journal of Approximate Reasoning, 2020, 117: 122-147.
[65] WANG Y, SUN B Z, ZHANG X R, et al. BWM and MULTIMOORA-Based Multigranulation Sequential Three-Way Decision Model for Multi-attribute Group Decision-Making Problem. International Journal of Approximate Reasoning, 2020, 125: 169-186.
[66] WANG M W, LIANG D C, XU Z S.Sequential Three-Way Multiple Attribute Group Decisions with Individual Attributes and Its Consensus Achievement Based on Social Influence. Information Sciences, 2020, 518: 286-308.
[67] SUN B Z, MA W M, XIAO X.Three-Way Group Decision Making Based on Multigranulation Fuzzy Decision-Theoretic Rough Set over Two Universes. International Journal of Approximate Reasoning, 2017, 81: 87-102.
[68] ZHANG C, LI D Y, ZHAI Y H, et al. Multigranulation Rough Set Model in Hesitant Fuzzy Information Systems and Its Application in Person-Job Fit. International Journal of Machine Learning and Cybernetics, 2019, 10: 717-729.
[69] SUN B Z, ZHOU X M, LIN N N.Diversified Binary Relation-Based Fuzzy Multigranulation Rough Set over Two Universes and Application to Multiple Attribute Group Decision Making. Information Fusion, 2020, 55: 91-104.
[70] LEI W J, MA W M, SUN B Z.Multigranulation Behavioral Three-Way Group Decisions under Hesitant Fuzzy Linguistic Environment. Information Sciences, 2020, 537: 91-115.
[71] DAI D, LI H X, JIA X Y, et al. A Co-training Approach for Sequential Three-Way Decisions. International Journal of Machine Learning and Cybernetics, 2020, 11(6): 1129-1139.
[72] ZHANG Q H, PANG G H, WANG G Y.A Novel Sequential Three-Way Decisions Model Based on Penalty Function. Know-ledge-Based Systems, 2020, 192. DOI: 10.1016/j.knosys.2019.105350.
[73] YANG X, LI T R, FUJITA H, et al. A Sequential Three-Way Approach to Multi-class Decision. International Journal of Approximate Reasoning, 2019, 104: 108-125.
[74] XU Y, TANG J X, WANG X S.Three Sequential Multi-class Three-Way Decision Models. Information Sciences, 2020, 537: 62-90.
[75] WANG Y, WANG Z, HU Q H, et al. Hierarchical Semantic Risk Minimization for Large-Scale Classification. IEEE Transactions on Cybernetics, 2021. DOI: 10.1109/TCYB.2021.3059631.
[76] YANG X B, QI Y, YU H L, et al. Updating Multigranulation Rough Approximations with Increasing of Granular Structures. Knowledge-Based Systems, 2014, 64: 59-69.
[77] HU C X, LIU S X, LIU G X.Matrix-Based Approaches for Dynamic Updating Approximations in Multigranulation Rough Sets. Knowledge-Based Systems, 2017, 122: 51-63.
[78] HU C X, LIU S X, HUANG X L.Dynamic Updating Approximations in Multigranulation Rough Sets While Refining or Coarsening Attribute Values. Knowledge-Based Systems, 2017, 130: 62-73.
[79] 罗贺,杨善林,丁帅.云计算环境下的智能决策研究综述.系统工程学报, 2013, 28(1): 134-142.
(LUO H, YANG S L, DING S.A Survey of Intelligent Decisions in Cloud Computing. Journal of Systems Engineering, 2013, 28(1): 134-142.) |
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2021, Vol. 34 
