大数据智能决策

于洪; 何德牛; 王国胤; 李劼; 谢永芳

doi:10.16383/j.aas.c180861

大数据智能决策

doi: 10.16383/j.aas.c180861

于洪^1, ,,
何德牛^1,,
王国胤^1,,
李劼^2,,
谢永芳^3,

1.
重庆邮电大学计算智能重庆市重点实验室重庆 400065
2.
中南大学冶金与环境学院长沙 410083
3.
中南大学信息科学与工程学院长沙 410083

基金项目:

国家自然科学基金 61751312

国家自然科学基金 61533020

国家自然科学基金 61876027

详细信息

作者简介:
何德牛  重庆邮电大学计算智能重庆市重点实验室博士研究生.主要研究方向为工业大数据智能决策, 三支决策, 粒计算. E-mail: hedeniu@163.com

王国胤  重庆邮电大学教授, 计算智能重庆市重点实验室主任.主要研究方向为粒计算, 知识发现, 认知计算, 智能信息处理, 大数据智能. E-mail: wanggy@ieee.org

李劼  中南大学教授.主要研究方向为冶金新技术与新材料, 冶金过程计算机仿真优化与智能控制.E-mail: 13808488404@163.com

谢永芳  中南大学教授.主要研究方向为复杂工业过程的建模与控制优化, 分布式鲁棒控制, 知识自动化. E-mail: yfxie@csu.edu.cn

通讯作者:
于洪重庆邮电大学教授.主要研究方向为工业大数据分析与处理, 智能决策, 知识发现, 粒计算, 三支聚类, 智能推荐.本文通信作者. E-mail: yuhong@cqupt.edu.cn

计量
- 文章访问数: 6822
- HTML全文浏览量: 2521
- PDF下载量: 1644
- 被引次数: 0
出版历程
- 收稿日期: 2018-12-29
- 录用日期: 2019-04-11
- 刊出日期: 2020-06-01

Big Data for Intelligent Decision Making

YU Hong^{1
, ,},
HE De-Niu^1
,,
WANG Guo-Yin^1
,,
LI Jie^2
,,
XIE Yong-Fang^3
,

1.
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065
2.
School of Metallurgy and Environment, Central South University, Changsha 410083
3.
School of Information Science and Engineering, Central South University, Changsha 410083

Funds:

National Natural Science Foundation of China 61751312

National Natural Science Foundation of China 61533020

National Natural Science Foundation of China 61876027

More Information

Author Bio:
HE De-Niu  Ph. D. candidate at Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications. His research interest covers industrial big data for intelligent decision making, three-way decisions and granular computing

WANG Guo-Yin  Professor at Chongqing University of Posts and Telecommunications, Dean of Chongqing Key Laboratory of Computational Intelligence. His research interest covers granular computing, knowledge discovery, cognitive computing, intelligent information processing and big data intelligence

LI Jie  Professor at Central South University. His research interest covers new technologies and materials for metallurgical, computer simulation optimization and intelligent control of metallurgical process

XIE Yong-Fang  Professor at Central South University. His research interest covers modeling and optimal control of complex industrial processes, distributed robust control and knowledge automation

Corresponding author: YU Hong Professor at Chongqing University of Posts and Telecommunications. Her research interest covers industrial big data analysis and processing, intelligent decision making, knowledge discovery, cognitive computing, granular computing, three-way clustering and intelligent recommendation. Corresponding author of this paper

摘要

摘要: 在全球信息化快速发展的背景下, 大数据已经成为一种战略资源.各行各业的决策活动在频度、广度及复杂性上较以往有着本质的不同.决策过程中的不确定性因素增多, 决策分析的难度不断加大.传统的数据分析方法以及基于人工经验的决策已难以满足大数据时代的决策需求, 大数据驱动的智能决策将成为决策研究的主旋律.该文结合大数据特性, 对大数据决策的特点进行了归纳, 并从智能决策支持系统、不确定性处理、信息融合、关联分析和增量分析等方面综述了大数据智能决策的研究与发展现状, 讨论了大数据智能决策依然面临的挑战, 并对一些潜在的研究方向进行了展望分析.
- 大数据 /
- 智能决策 /
- 不确定性 /
- 信息融合 /
- 关联分析 /
- 增量式学习
Abstract: As a result of globalization and informatization, big data has become one kind of important strategic resources. Decision-making activities in all walks of life are different from the past in frequency, breadth and complexity. The difficulty of decision analysis is increased due to the increase of uncertainty factors in the decision-making process. The decision analysis methods based on the traditional data analysis methods or manual experiences are gradually unable to meet the needs of decision-making in the era of big data. We think the intelligent decision making methods based on big data driven will become an important solution. This paper presents the characteristics of big data for intelligent decision making in view of analyzing the features of big data. Some recent theoretic studies and applications of intelligent decision-making systems, uncertainty intelligent decision making, methods based on information fusion, methods based on association analysis and incremental learning are reviewed. The paper also points out the future perspectives and potential research points.
- Big data /
- intelligent decision-making /
- uncertainty /
- information fusion /
- association analysis /
- incremental learning
Recommended by Associate Editor ZHANG Min-Ling
注释:

1) 本文责任编委张敏灵

HTML全文

图 1 跨领域大数据融合范式^[73]

Fig. 1 The paradigm of cross-domain big data fusion^[73]

下载: 全尺寸图片幻灯片

表 1 不同层次下数据融合对比情况表

Table 1 Comparison of data fusion under different levels

融合层次	常用融合机制	优缺点
融合层次	常用融合机制	处理复杂度	信息损失	性能损失	传输负载
数据层	竞争型	高	否	否	高
特征层	竞争, 互补, 合作	中等	是	是	中等
决策层	竞争, 互补, 合作	低到高	是	是	低

下载: 导出CSV

参考文献(143)

[1]	Big Data. Nature [Online], available: http://www.nature.com/news/specials/bigdata/index.html, April 12, 2019.
[2]	World Economic Forum. Big data, big impact: new possibilities for international development [online], available: http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf, April 12, 2019.
[3]	United Nations Global Pulse. Big data for development: opportunities and Challenges—White Paper [online], available: http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf, April 12, 2019.
[4]	Tolle K M, Tansley D S W, Hey A J G. The fourth paradigm: data-intensive scientific discovery. Proceedings of the IEEE, 2011, 99(8): 1334-1337 doi: 10.1109/JPROC.2011.2155130
[5]	Zhu K P, Joshi S, Wang Q G, Hsi J F Y. Guest editorial special section on big data analytics in intelligent manufacturing. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2382-2385 doi: 10.1109/TII.2019.2900726
[6]	杨善林, 周开乐.大数据中的管理问题:基于大数据的资源观.管理科学学报, 2015, 18(5): 1-8 doi: 10.3969/j.issn.1007-9807.2015.05.001 Yang Shan-Lin, Zhou Kai-Le. Management issues in Big Data: the resource-based view of Big Data. Journal of Management Sciences in China, 2015, 18(5): 1-8 doi: 10.3969/j.issn.1007-9807.2015.05.001
[7]	Hubbard D W. How to Measure Anything: finding the Value of "Intangibles" in Business. New Jersey: Wiley, 2010.
[8]	Provost F, Fawcett T. Data science and its relationship to big data and data-driven decision making. Big Data, 2013, 1(1): 51-59 doi: 10.1089/big.2013.1508
[9]	陈纯, 庄越挺.大数据智能:从数据到知识与决策.中国科技财富, 2017, (8): 48-49 doi: 10.3969/j.issn.1671-461X.2017.08.019 Chen Chun, Zhuang Yue-Ting. Big data intelligence: from data to knowledge and decisions, Fortune World, 2017, (8): 48-49 doi: 10.3969/j.issn.1671-461X.2017.08.019
[10]	高婴劢.工业大数据价值挖掘路径.中国工业评论, 2015, (2): 21-27 http://d.old.wanfangdata.com.cn/Periodical/zgjjhxxh201502004 Gao Ying-Mai. Industrial big data value mining path. China Industry Review, 2015, (2): 21-27 http://d.old.wanfangdata.com.cn/Periodical/zgjjhxxh201502004
[11]	Chen C L P, Zhang C Y. Data-intensive applications, challenges, techniques and technologies: a survey on big data. Information Sciences, 2014, 275(11): 314-347 http://cn.bing.com/academic/profile?id=a92023ca16b20dca9d422b30fc7613b0&encoded=0&v=paper_preview&mkt=zh-cn
[12]	工业大数据白皮书(2019版) [online], available: http://www.cesi.cn/201904/4955.html, 2019年4月1日 Industrial Big Data White Paper (2019 edition) [online], available: http://www.cesi.cn/201904/4955.html, April 1, 2019 (in Chinese)
[13]	吴信东, 何进, 陆汝钤, 郑南宁.从大数据到大知识: HACE + BigKE.自动化学报, 2016, 42(7): 965-982 doi: 10.16383/j.aas.2016.c160239 Wu Xin-Dong, He Jin, Lu Ru-Qian, Zheng Nan-Ning. From big data to big knowledge: HACE +BigKE. Acta Automatica Sinica, 2016, 42(7): 965-982 doi: 10.16383/j.aas.2016.c160239
[14]	刘强, 秦泗钊.过程工业大数据建模研究展望.自动化学报, 2016, 42(2): 161-171 doi: 10.16383/j.aas.2016.c150510 Liu Qiang, Qin Si-Zhao. Perspectives on big data modeling of process industries. Acta Automatica Sinica, 2016, 42(2): 161-171 doi: 10.16383/j.aas.2016.c150510
[15]	Wang X Z, He Y L. Learning from uncertainty for Big Data: future analytical challenges and strategies. IEEE Systems, Man, and Cybernetics Magazine, 2016, 2(2): 26-31 http://cn.bing.com/academic/profile?id=40f2a77b42d9ff9948b94976f884390a&encoded=0&v=paper_preview&mkt=zh-cn
[16]	Wu X D, Zhu X Q, Wu G Q, Ding W. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(1): 97-107 http://d.old.wanfangdata.com.cn/Periodical/kfjyyj201801001
[17]	谢新水.多元价值、大数据与决策不确定性的应对策略.北京工商大学学报(社会科学版), 2014, 29(6): 109-114 doi: 10.3969/j.issn.1009-6116.2014.06.014 Xie Xin-Shui. Multiple values, big data and coping strategies against decision-making uncertainty. Journal of Beijing Technology and Business University (Social Sciences), 2014, 29(6): 109-114 doi: 10.3969/j.issn.1009-6116.2014.06.014
[18]	梁吉业, 冯晨娇, 宋鹏.大数据相关分析综述.计算机学报, 2016, 39(1): 1-18 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201601001 Liang Ji-Ye, Feng Chen-Jiao, Song Peng. A survey on correlation analysis of big data. Chinese Journal of Computers, 2016, 39(1): 1-18 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201601001
[19]	Ginsberg J, Mohebbi M H, Patel R S, Brammer L, Smolinski M S, Brilliant L. Detecting influenza epidemics using search engine query data. Nature, 2009, 457(7232): 1012-1014 doi: 10.1038/nature07634
[20]	Böttger T, Cuadrado F, Tyson G, Castro I, Uhlig S. Open connect everywhere: a glimpse at the internet ecosystem through the lens of the netflix CDN. ACM SIGCOMM Computer Communication Review, 2018, 48(1): 28-34 doi: 10.1145/3211852.3211857
[21]	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 doi: 10.3969/j.issn.1000-5781.2013.01.018 Luo He, Yang Shan-lin, Ding Shuai. A survey of intelligent decisions in cloud computing. Journal of Systems Engineering, 2013, 28(1): 134-142 doi: 10.3969/j.issn.1000-5781.2013.01.018
[22]	Scott-Morton M S. Management Decision Systems: Computer Based Support for Decision Making. Boston: Harvard University, 1971. 30-80
[23]	Sprague Jr R H. A framework for the development of decision support systems. MIS quarterly, 1980: 1-26
[24]	Bonczek R H, Holsapple C W, Whinston A B. The evolving roles of models in decision support systems. Decision Sciences, 1980, 11(2): 337-356 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1111/j.1540-5915.1980.tb01143.x
[25]	任明仑, 杨善林, 朱卫东.智能决策支持系统:研究现状与挑战.系统工程学报, 2002, 17(5): 430-440 doi: 10.3969/j.issn.1000-5781.2002.05.008 Ren Ming-Lun, Yang Shan-Lin, Zhu Wei-Dong. Intelligent decision support system: state of art and challenges. Journal of Systems Engineering, 2002, 17(5): 430-440 doi: 10.3969/j.issn.1000-5781.2002.05.008
[26]	Gray P. Group decision support systems. Decision Support Systems, 1987, 3(3): 233-242 doi: 10.1016/0167-9236(87)90178-3
[27]	Liang D C, Liu D, Kobina A. Three-way group decisions with decision-theoretic rough sets. Information Sciences, 2016, 345: 46-64 doi: 10.1016/j.ins.2016.01.065
[28]	Manheim M L. An architecture for active DSS. In: Proceedings of the 21st Annual Hawaii International Conference on System Sciences. Kailua-Kona, USA: IEEE, 1988, 3: 356-365
[29]	Shaw M J. Machine learning methods for intelligent decision support An introduction. Decision Support Systems, 1993, 10(2): 79-83 doi: 10.1016/0167-9236(93)90031-W
[30]	Mayer M K. Future trends in model management systems: parallel and distributed extensions. Decision Support Systems, 1998, 22(4): 325-335 http://cn.bing.com/academic/profile?id=2c2ec6d5524fea5748aac58c4661025a&encoded=0&v=paper_preview&mkt=zh-cn
[31]	Bui T, Lee J. An agent-based framework for building decision support systems. Decision Support Systems, 1999, 25(3): 225-237 doi: 10.1016/S0167-9236(99)00008-1
[32]	Ghadimi P, Toosi F G, Heavey C. A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain. European Journal of Operational Research, 2018, 269(1): 286-301 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=e6c1bb29c652e045e498b492d374cef3
[33]	Shi Y, Chen S Z, Xu X. MAGA: a mobility-aware computation offloading decision for distributed mobile cloud computing. IEEE Internet of Things Journal, 2018, 5(1): 164-174 doi: 10.1109/JIOT.2017.2776252
[34]	王国胤, 张清华, 马希骜, 杨青山.知识不确定性问题的粒计算模型.软件学报, 2011, 22(4): 676-694 http://d.old.wanfangdata.com.cn/Periodical/rjxb201104006 Wang Guo-Yin, Zhang Qing-Hua, Ma Xi-Ao, Yang Qing-Shan. Granular computing models for knowledge uncertainty. Journal of Software, 2011, 22(4): 676-694 http://d.old.wanfangdata.com.cn/Periodical/rjxb201104006
[35]	Wang H, Xu Z S, Pedrycz W. An overview on the roles of fuzzy set techniques in big data processing: trends, challenges and opportunities. Knowledge-Based Systems, 2017, 118: 15-30 doi: 10.1016/j.knosys.2016.11.008
[36]	Liu C F, Huang W B, Sun F C, Luo M N, Tan C Q. LDS-FCM: A linear dynamical system based fuzzy c-means method for tactile recognition. IEEE Transactions on Fuzzy Systems, 2019, 27(1): 72-83 doi: 10.1109/TFUZZ.2018.2859184
[37]	Chang X Y, Wang Q N, Liu Y W, Wang Y. Sparse regularization in fuzzy c-means for high-dimensional data clustering. IEEE Transactions on Cybernetics, 2017, 47(9): 2616-2627 doi: 10.1109/TCYB.2016.2627686
[38]	Di Martino F, Sessa S. Extended fuzzy C-means hotspot detection method for large and very large event datasets. Information Sciences, 2018, 441: 198-215 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c9b7103075279b7fba03cb74ca247d18
[39]	Di Martino F, Pedrycz W, Sessa S. Spatiotemporal extended fuzzy C-means clustering algorithm for hotspots detection and prediction. Fuzzy Sets and Systems, 2018, 340: 109-126 doi: 10.1016/j.fss.2017.11.011
[40]	Jindal A, Dua A, Kumar N, Vasilakos A V, Rodrigues J J P C. An efficient fuzzy rule-based big data analytics scheme for providing healthcare-as-a-service. In: Proceedings of the 2017 IEEE International Conference on Communications. Paris, France: IEEE, 2017. 1-6
[41]	Segatori A, Marcelloni F, Pedrycz W. On distributed fuzzy decision trees for big data. IEEE Transactions on Fuzzy Systems, 2018, 26(1): 174-192 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=WK_MED202003022318
[42]	Jayawardene I, Venayagamoorthy G K. Comparison of adaptive neuro-fuzzy inference systems and echo state networks for PV power prediction. Procedia Computer Science, 2015, 53: 92-102 http://cn.bing.com/academic/profile?id=7e1f1a1d6fa77046db7d653547d320d0&encoded=0&v=paper_preview&mkt=zh-cn
[43]	Jindal A, Dua A, Kumar N, Das A K, Vasilakos A V, Rodrigues J J P C. Providing healthcare-as-a-service using fuzzy rule based big data analytics in cloud computing. IEEE Journal of Biomedical and Health Informatics, 2018, 22(5): 1605-1618 doi: 10.1109/JBHI.2018.2799198
[44]	Qian J, Lv P, Yue X D, Liu C H, Jing Z J. Hierarchical attribute reduction algorithms for big data using MapReduce. Knowledge-Based Systems, 2015, 73: 18-31 doi: 10.1016/j.knosys.2014.09.001
[45]	Li S Y, Li T R, Zhang Z X, Chen H M, Zhang J B. Parallel computing of approximations in dominance-based rough sets approach. Knowledge-Based Systems, 2015, 87: 102-111 doi: 10.1016/j.knosys.2015.05.003
[46]	Abdel-Basset M, Mohamed M. The role of single valued neutrosophic sets and rough sets in smart city: imperfect and incomplete information systems. Measurement, 2018, 124: 47-55 doi: 10.1016/j.measurement.2018.04.001
[47]	El-Alfy E S M, Alshammari M A. Towards scalable rough set based attribute subset selection for intrusion detection using parallel genetic algorithm in MapReduce. Simulation Modelling Practice and Theory, 2016, 64: 18-29 doi: 10.1016/j.simpat.2016.01.010
[48]	Banerjee S, Badr Y. Evaluating decision analytics from mobile big data using rough set based ant colony. Mobile Big Data. Cham: Springer, 2018. 217-231
[49]	Hu Q H, Zhang L J, Zhou Y C, Pedrycz W. Large-scale multimodality attribute reduction with multi-kernel fuzzy rough sets. IEEE Transactions on Fuzzy Systems, 2018, 26(1): 226-238 doi: 10.1109/TFUZZ.2017.2647966
[50]	Qian Y H, Liang X Y, Lin G P, Guo Q, Liang J Y. Local multigranulation decision-theoretic rough sets. International Journal of Approximate Reasoning, 2017, 82: 119-137 doi: 10.1016/j.ijar.2016.12.008
[51]	Qian Y H, Liang X Y, Wang Q, Liang J Y, Liu B, Skowron A, et al. Local rough set: a solution to rough data analysis in big data. International Journal of Approximate Reasoning, 2018, 97: 38-63 http://cn.bing.com/academic/profile?id=396536b70540c2cb303bc14d7408a25f&encoded=0&v=paper_preview&mkt=zh-cn
[52]	Luo C, Li T R, Huang Y Y, Fujita H. Updating three-way decisions in incomplete multi-scale information systems. Information Sciences, 2019, 476: 274-289 doi: 10.1016/j.ins.2018.10.012
[53]	Yao J T, Azam N. Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Transactions on Fuzzy Systems, 2015, 23(1): 3-15 doi: 10.1109/TFUZZ.2014.2360548
[54]	Yu H, Wang X C, Wang G Y, Zeng X H. An active three-way clustering method via low-rank matrices for multi-view data. Information Sciences, 2020, 507: 823-839 doi: 10.1016/j.ins.2018.03.009
[55]	Zhang H Y, Yang S Y. Three-way group decisions with interval-valued decision-theoretic rough sets based on aggregating inclusion measures. International Journal of Approximate Reasoning, 2019, 110: 31-45 doi: 10.1016/j.ijar.2019.03.011
[56]	Li H X, Zhang L B, Huang B, Zhou X Z. Sequential three-way decision and granulation for cost-sensitive face recognition. Knowledge-Based Systems, 2016, 91: 241-251 doi: 10.1016/j.knosys.2015.07.040
[57]	Qian J, Liu C H, Miao D Q, Yue X D. Sequential three-way decisions via multi-granularity. Information Sciences, 2020, 507: 606-629. doi: 10.1016/j.ins.2019.03.052
[58]	Lake B M, Salakhutdinov R, Tenenbaum J B. Human-level concept learning through probabilistic program induction. Science, 2015, 350(6266): 1332-1338 doi: 10.1126/science.aab3050
[59]	Sturlaugson L, Sheppard J W. Uncertain and negative evidence in continuous time Bayesian networks. International Journal of Approximate Reasoning, 2016, 70: 99-122 doi: 10.1016/j.ijar.2015.12.013
[60]	Abadpour A. Rederivation of the fuzzy-possibilistic clustering objective function through Bayesian inference. Fuzzy Sets and Systems, 2016, 305: 29-53 doi: 10.1016/j.fss.2015.10.005
[61]	胡支军, 彭飞, 李志霞.风险项目投资组合决策的贝叶斯评价与选择策略.中国管理科学, 2017, 25(2): 30-39 http://d.old.wanfangdata.com.cn/Periodical/zgglkx201702004 Hu Zhi-Jun, Peng Fei, Li Zhi-Xia. Bayesian evaluation and selection strategies in venture project portfolio decision analysis. Chinese Journal of Management Science, 2017, 25(2): 30-39 http://d.old.wanfangdata.com.cn/Periodical/zgglkx201702004
[62]	Hao Z N, Xu Z S, Zhao H, Fujita H. A dynamic weight determination approach based on the intuitionistic fuzzy bayesian network and its application to emergency decision making. IEEE Transactions on Fuzzy Systems, 2018, 26(4): 1893-1907 doi: 10.1109/TFUZZ.2017.2755001
[63]	Li N, Feng X D, Jimenez R. Predicting rock burst hazard with incomplete data using Bayesian networks. Tunnelling and Underground Space Technology, 2017, 61: 61-70 doi: 10.1016/j.tust.2016.09.010
[64]	Feng X D, Jimenez R. Predicting tunnel squeezing with incomplete data using Bayesian networks. Engineering Geology, 2015, 195: 214-224 doi: 10.1016/j.enggeo.2015.06.017
[65]	Zhang M J, Wang Y M, Li L H, Chen S Q. A general evidential reasoning algorithm for multi-attribute decision analysis under interval uncertainty. European Journal of Operational Research, 2017, 257(3): 1005-1015 doi: 10.1016/j.ejor.2016.08.028
[66]	Sun L, Wang Y Z. A multi-attribute fusion approach extending Dempster-Shafer theory for combinatorial-type evidences. Expert Systems with Applications, 2018, 96: 218-229 doi: 10.1016/j.eswa.2017.12.005
[67]	Troiano L, Rodríguez-Muñiz L J, Díaz I. Discovering user preferences using Dempster-Shafer theory. Fuzzy Sets and Systems, 2015, 278: 98-117 doi: 10.1016/j.fss.2015.06.004
[68]	杜元伟, 段万春, 黄庆华, 杨娜.基于头脑风暴原则的主观证据融合决策方法.中国管理科学, 2015, 23(3): 130-140 http://d.old.wanfangdata.com.cn/Periodical/zgglkx201503016 Du Yuan-Wei, Duan Wan-Chun, Huang Qing-Hua, Yang Na. Decision making method for integrating subjective evidences based on brain storming principles. Chinese Journal of Management Science, 2015, 23(3): 130-140 http://d.old.wanfangdata.com.cn/Periodical/zgglkx201503016
[69]	Bukharov O E, Bogolyubov D P. Development of a decision support system based on neural networks and a genetic algorithm. Expert Systems with Applications, 2015, 42(15-16): 6177-6183 doi: 10.1016/j.eswa.2015.03.018
[70]	Yu H, Zhou Q F, Liu M. A dynamic composite web services selection method with QoS-Aware based on AND/OR graph. International Journal of Computational Intelligence Systems, 2014, 7(4): 660-675 doi: 10.1080/18756891.2014.960226
[71]	罗俊海, 王章静.多源数据融合和传感器管理.北京:清华大学出版社, 2015. Luo Jun-Hai, Wang Zhang-Jing. Multi-Source Data Fusion and Sensor Management. Beijing: Tsinghua University Press, 2015
[72]	Khaleghi B, Khamis A, Karray F O, Razavi S N. Multisensor data fusion: a review of the state-of-the-art. Information Fusion, 2013, 14(1): 28-44 doi: 10.1016/j.inffus.2011.08.001
[73]	Zheng Y. Methodologies for cross-domain data fusion: an overview. IEEE Transactions on Big Data, 2015, 1(1): 16-34 doi: 10.1109/TBDATA.2015.2465959
[74]	Chen Z G, Li Y G, Chen X F, Yang C H, Gui W H. Semantic network based on intuitionistic fuzzy directed hyper-graphs and application to aluminum electrolysis cell condition identification. IEEE Access, 2017, 5: 20145-20156 doi: 10.1109/ACCESS.2017.2752200
[75]	Gravina R, Alinia P, Ghasemzadeh H, Fortino G. Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Information Fusion, 2017, 35: 68-80 doi: 10.1016/j.inffus.2016.09.005
[76]	Chang N B, Bai K X, Imen S, Chen C F, Gao W. Multisensor satellite image fusion and networking for all-weather environmental monitoring. IEEE Systems Journal, 2018, 12(2): 1341-1357 doi: 10.1109/JSYST.2016.2565900
[77]	覃雄派, 王会举, 杜小勇, 王珊.大数据分析—RDBMS与MapReduce的竞争与共生.软件学报, 2012, 23(1): 32-45 http://d.old.wanfangdata.com.cn/Periodical/jsjgprjyyy201307040 Qin Xiong-Pai, Wang Hui-Ju, Du Xiao-Yong, Wang Shan. Big Data analysis—competition and symbiosis of RDBMS and MapReduce. Journal of Software, 2012, 23(1): 32-45 http://d.old.wanfangdata.com.cn/Periodical/jsjgprjyyy201307040
[78]	Huang Z R, Wang P, Zhang F, Gao J X, Schich M. A mobility network approach to identify and anticipate large crowd gatherings. Transportation Research Part B: Methodological, 2018, 114: 147-170 doi: 10.1016/j.trb.2018.05.016
[79]	Lin Y J, Chen H H, Lin G P, Chen J K, Ma Z M, Li J J. Synthesizing decision rules from multiple information sources: a neighborhood granulation viewpoint. International Journal of Machine Learning and Cybernetics, 2018, 9(11): 1919-1928 doi: 10.1007/s13042-018-0791-z
[80]	Kiros R, Salakhutdinov R, Zemel R. Multimodal neural language models. In: Proceedings of the 31st International Conference on Machine Learning. Beijing, China: IMLS, 2014. 595-603
[81]	Srivastava N, Salakhutdinov R. Multimodal learning with deep boltzmann machines. In: Proceedings of the 26th Annual Conference on Neural Information Processing Systems. Lake Tahoe, USA: IEEE, 2012. 2222-2230
[82]	Xu W H, Yu J H. A novel approach to information fusion in multi-source datasets: a granular computing viewpoint. Information Sciences, 2017, 378: 410-423 doi: 10.1016/j.ins.2016.04.009
[83]	Galton F. Co-relations and their measurement, chiefly from anthropometric data. Proceedings of the Royal Society of London, 1889, 45(273-279): 135-145 doi: 10.1098/rspl.1888.0082
[84]	李国杰, 程学旗.大数据研究:未来科技及经济社会发展的重大战略领域—大数据的研究现状与科学思考.中国科学院院刊, 2012, 27(6): 647-657 doi: 10.3969/j.issn.1000-3045.2012.06.001 Li Guo-Jie, Cheng Xue-Qi. Research status and scientific thinking of Big Data. Bulletin of Chinese Academy of Sciences, 2012, 27(6): 647-657 doi: 10.3969/j.issn.1000-3045.2012.06.001
[85]	Mayer-Schonberger V, Cukier K. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston: Houghton Mifflin Harcourt, 2013.
[86]	Ye J. Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. International Journal of General Systems, 2013, 42(4): 386-394 doi: 10.1080/03081079.2012.761609
[87]	Liao H C, Xu Z S, Zeng X J, Merigó J M. Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowledge-Based Systems, 2015, 76: 127-138 doi: 10.1016/j.knosys.2014.12.009
[88]	Pei S L, Hu Q H. Partially monotonic decision trees. Information Sciences, 2018, 424: 104-117 doi: 10.1016/j.ins.2017.10.006
[89]	Yang Y, Ma Z G, Yang Y, Nie F P, Shen H T. Multitask spectral clustering by exploring intertask correlation. IEEE Transactions on Cybernetics, 2015, 45(5): 1083-1094. doi: 10.1109/TCYB.2014.2344015
[90]	Wang Y, Lin X M, Wu L, Zhang W J, Zhang Q, Huang X D. Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Transactions on Image Processing, 2015, 24(11): 3939-3949 doi: 10.1109/TIP.2015.2457339
[91]	Ma H F, Jia M H Z, Zhang D, Lin X H. Combining tag correlation and user social relation for microblog recommendation. Information Sciences, 2017, 385: 325-337 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c6f8fe2faddfcbd5d0f4ff89a45ab2e7
[92]	Zhu Y, Kwok J T, Zhou Z H. Multi-label learning with global and local label correlation. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(6): 1081-1094 doi: 10.1109/TKDE.2017.2785795
[93]	Chaudhuri K, Kakade S M, Livescu K, Sridharan K. Multi-view clustering via canonical correlation analysis. In: Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Canada: ACM, 2009. 129-136
[94]	孙权森, 曾生根, 王平安, 夏德深.典型相关分析的理论及其在特征融合中的应用.计算机学报, 2005, 28(9): 1524-1533 doi: 10.3321/j.issn:0254-4164.2005.09.015 Sun Quan-Sen, Zeng Sheng-Gen, Wang Ping-An, Xia De-Shen. The theory of canonical correlation analysis and its application to feature fusion. Chinese Journal of Computers, 2005, 28(9): 1524-1533 doi: 10.3321/j.issn:0254-4164.2005.09.015
[95]	杨静, 李文平, 张健沛.基于秩2更新的多维数据流典型相关跟踪算法.电子学报, 2012, 40(9): 1765-1774 http://d.old.wanfangdata.com.cn/Periodical/dianzixb201209011 Yang Jing, Li Wen-Ping, Zhang Jian-Pei. A tracking algorithm based on rank two modifications for canonical correlation analysis of multidimensional data streams. Acta Electronica Sinica, 2012, 40(9): 1765-1774 http://d.old.wanfangdata.com.cn/Periodical/dianzixb201209011
[96]	Rasiwasia N, Costa Pereira J, Coviello E, Doyle G, Lanckriet G R G, Levy R, et al. A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th ACM International Conference on Multimedia. Firenze, Italy: ACM, 2010. 251-260
[97]	Yin X R. Canonical correlation analysis based on information theory. Journal of Multivariate Analysis, 2004, 91(2): 161-176 doi: 10.1016/S0047-259X(03)00129-5
[98]	Lai P L, Fyfe C. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 2000, 10(5): 365-377 doi: 10.1142/S012906570000034X
[99]	Hardoon D R, Szedmak S, Shawe-Taylor J. Canonical correlation analysis: an overview with application to learning methods. Neural Computation, 2004, 16(12): 2639-2664 doi: 10.1162/0899766042321814
[100]	杨静, 李文平, 张健沛.大数据典型相关分析的云模型方法.通信学报, 2013, 34(10): 121-134 doi: 10.3969/j.issn.1000-436x.2013.10.015 Yang Jing, Li Wen-Ping, Zhang Jian-Pei. Canonical correlation analysis of big data based on cloud model. Journal on Communications, 2013, 34(10): 121-134 doi: 10.3969/j.issn.1000-436x.2013.10.015
[101]	Reshef D N, Reshef Y A, Finucane H K, Grossman S R, McVean G, Turnbaugh P J, et al. Detecting novel associations in large data sets. Science, 2011, 334(6062): 1518-1524 doi: 10.1126/science.1205438
[102]	Nguyen H V, Müller E, Vreeken J, Efros P, Böhm K. Multivariate maximal correlation analysis. In: Proceedings of the 31st International Conference on Machine Learning. Beijing, China: W & CP, 2014. 775-783
[103]	Székely G J, Rizzo M L, Bakirov N K. Measuring and testing dependence by correlation of distances. The Annals of Statistics, 2007, 35(6): 2769-2794 doi: 10.1214/009053607000000505
[104]	Martínez-Gómez E, Richards M T, Richards D S P. Distance correlation methods for discovering associations in large astrophysical databases. The Astrophysical Journal, 2014, 781(1): 39 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_1308.3925
[105]	Davis R A, Matsui M, Mikosch T, Wan P. Applications of distance correlation to time series. Bernoulli, 2018, 24(4A): 3087-3116 doi: 10.3150/17-BEJ955
[106]	林子雨, 江弋, 赖永炫, 林琛.一种新的时间序列延迟相关性分析算法—三点预测探查法.计算机研究与发展, 2012, 49(12): 2645-2655 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjyjyfz201212016 Lin Zi-Yu, Jiang Yi, Lai Yong-Xuan, Lin Chen. A new algorithm on lagged correlation analysis between time series: TPFP. Journal of Computer Research and Development, 2012, 49(12): 2645-2655 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjyjyfz201212016
[107]	姜高霞, 王文剑.时序数据曲线排齐的相关性分析方法.软件学报, 2014, 25(9): 2002-2017 http://d.old.wanfangdata.com.cn/Periodical/rjxb201409009 Jiang Gao-Xia, Wang Wen-Jian. Correlation analysis in curve registration of time series. Journal of Software, 2014, 25(9): 2002-2017 http://d.old.wanfangdata.com.cn/Periodical/rjxb201409009
[108]	张文凯, 王文剑, 姜高霞.基于非均匀采样的相关系数最大化曲线排齐方法.模式识别与人工智能, 2016, 29(1): 72-81 http://d.old.wanfangdata.com.cn/Periodical/mssbyrgzn201601009 Zhang Wen-Kai, Wang Wen-Jian, Jiang Gao-Xia. Curve registration method for maximizing correlation coefficient based on non-uniform sampling. Pattern Recognition and Artificial Intelligence, 2016, 29(1): 72-81 http://d.old.wanfangdata.com.cn/Periodical/mssbyrgzn201601009
[109]	Zhao J P, Itti L. Shapedtw: shape dynamic time warping. Pattern Recognition, 2018, 74: 171-184 doi: 10.1016/j.patcog.2017.09.020
[110]	Baldocchi D, Sturtevant C, Contributors F. Does day and night sampling reduce spurious correlation between canopy photosynthesis and ecosystem respiration? Agricultural and Forest Meteorology, 2015, 207: 117-126 doi: 10.1016/j.agrformet.2015.03.010
[111]	Clappe S, Dray S, Peres-Neto P R. Beyond neutrality: disentangling the effects of species sorting and spurious correlations in community analysis. Ecology, 2018, 99(8): 1737-1747 doi: 10.1002/ecy.2376
[112]	Gao P, Zhang L J. Determining spurious correlation between two variables with common elements: event area-weighted suspended sediment yield and event mean runoff depth. The Professional Geographer, 2016, 68(2): 261-270 doi: 10.1080/00330124.2015.1065548
[113]	Altman N, Krzywinski M. Association, correlation and causation. Nature Methods, 2015, 12(10): 899-900 doi: 10.1038/nmeth.3587
[114]	Xu J, Xu C, Zou B, Tang Y Y, Peng J T, You X G. New incremental learning algorithm with support vector machines. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(11): 2230-2241 doi: 10.1109/TSMC.2018.2791511
[115]	Gu B, Quan X, Gu Y H, Sheng V S, Zheng G S. Chunk incremental learning for cost-sensitive hinge loss support vector machine. Pattern Recognition, 2018, 83: 196-208 doi: 10.1016/j.patcog.2018.05.023
[116]	Chen H M, Li T R, Zhang J B. A method for incremental updating approximations based on variable precision set-valued ordered information systems. In: Proceedings of the 2010 IEEE International Conference on Granular Computing. San Jose, USA: IEEE, 2010. 96-101
[117]	Li S Y, Li T R. Incremental update of approximations in dominance-based rough sets approach under the variation of attribute values. Information Sciences, 2015, 294: 348-361 doi: 10.1016/j.ins.2014.09.056
[118]	Yu H. Three-way decisions and three-way clustering. In: Proceedings of the 2008 International Joint Conference on Rough Sets. Quy Nhon, Vietnam: Springer, 2018. 13-28
[119]	Hu J, Li T R, Luo C, Fujita H, Yang Y. Incremental fuzzy cluster ensemble learning based on rough set theory. Knowledge-Based Systems, 2017, 132: 144-155 doi: 10.1016/j.knosys.2017.06.020
[120]	Hu C, Chen Y, Peng X, et al. A novel feature incremental learning method for sensor-based activity recognition. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(6): 1038-1050 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4e5b7508e7038c3b846f539a4f6f90b5
[121]	Huang Y Y, Li T R, Luo C, Horng S J. Dynamic updating rough approximations in distributed information systems. In: Proceedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering. Taipei, China: IEEE, 2015. 170-175
[122]	Jing Y G, Li T R, Fujita H, Yu Z, Wang B. An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view. Information Sciences, 2017, 411: 23-38 doi: 10.1016/j.ins.2017.05.003
[123]	Luo C, Li T R, Chen H M, Fujita H, Yi Z. Incremental rough set approach for hierarchical multicriteria classification. Information Sciences, 2018, 429: 72-87 doi: 10.1016/j.ins.2017.11.004
[124]	Da Q, Yu Y, Zhou Z H. Learning with augmented class by exploiting unlabeled data. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec, Canada: AAAI Press, 2014. 1760-1766
[125]	Ristin M, Guillaumin M, Gall J, Van Gool L. Incremental learning of NCM forests for large-scale image classification. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014. 3654-3661
[126]	Ristin M, Guillaumin M, Gall J, Van Gool L. Incremental learning of random forests for large-scale image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(3): 490-503 doi: 10.1109/TPAMI.2015.2459678
[127]	Júnior P R M, de Souza R M, de O. Werneck R, Stein B V, Pazinato D V, de Almeida W R, et al. Nearest neighbors distance ratio open-set classifier. Machine Learning, 2017, 106(3): 359-386 doi: 10.1007/s10994-016-5610-8
[128]	Neal L, Olson M, Fern X L, Wong W K, Li F X. Open set learning with counterfactual images. In: Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018. 620-635
[129]	Bendale A, Boult T E. Towards open set deep networks. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 1563-1572
[130]	Liang S Y, Li Y X, Srikant R. Enhancing the reliability of out-of-distribution image detection in neural networks [online], available: https://arxiv.org/abs/1706.02690, December 20, 2018
[131]	Ahmad S, Lavin A, Purdy S, Agha Z. Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 2017, 262: 134-147 doi: 10.1016/j.neucom.2017.04.070
[132]	Dong F, Zhang G Q, Lu J, Li K. Fuzzy competence model drift detection for data-driven decision support systems. Knowledge-Based Systems, 2018, 143: 284-294 doi: 10.1016/j.knosys.2017.08.018
[133]	Lobo J L, Del Ser J, Bilbao M N, Perfecto C, Salcedo-Sanz S. DRED: an evolutionary diversity generation method for concept drift adaptation in online learning environments. Applied Soft Computing, 2018, 68: 693-709 doi: 10.1016/j.asoc.2017.10.004
[134]	于洪, 王国胤, 李天瑞, 梁吉业, 苗夺谦, 姚一豫.三支决策:复杂问题求解方法与实践.北京:科学出版社, 2015. Yu Hong, Wang Guo-Yin, Li Tian-Rui, Liang Ji-Ye, Miao Duo-Qian, Yao Yi-Yu. Three-Way Decisions: Methods and Practices for Complex Problem Solving. Beijing: Science Press, 2015.
[135]	苗夺谦, 张清华, 钱宇华, 梁吉业, 王国胤, 吴伟志, 等.从人类智能到机器实现模型—粒计算理论与方法.智能系统学报, 2016, 11(6): 743-757 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xdkjyc201606005 Miao Duo-Qian, Zhang Qing-Hua, Qian Yu-Hua, Liang Ji-Ye, Wang Guo-Yin, Wu Wei-Zhi, et al. From human intelligence to machine implementation model: theories and applications based on granular computing. CAAI Transactions on Intelligent Systems, 2016, 11(6): 743-757 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xdkjyc201606005
[136]	徐计, 王国胤, 于洪.基于粒计算的大数据处理.计算机学报, 2015, 38(8): 1497-1517 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201508001 Xu Ji, Wang Guo-Yin, Yu Hong. Review of big data processing based on granular computing. Chinese Journal of Computers, 2015, 38(8): 1497-1517 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201508001
[137]	Lee J, Jung J, Park P, Chung S, Cha H. Design of a human-centric de-identification framework for utilizing various clinical research data. Human-centric Computing and Information Sciences, 2018, 8(1): 19 doi: 10.1186/s13673-018-0142-9
[138]	Wang G Y, Yang J, Xu J. Granular computing: from granularity optimization to multi-granularity joint problem solving. Granular Computing, 2017, 2(3): 105-120 doi: 10.1007/s41066-016-0032-3
[139]	Wang G Y. DGCC: data-driven granular cognitive computing. Granular Computing, 2017, 2(4): 343-355, 514 doi: 10.1007/s41066-017-0048-3
[140]	王飞跃.平行系统方法与复杂系统的管理和控制.控制与决策, 2004, 19(5): 485-489, 514 doi: 10.3321/j.issn:1001-0920.2004.05.002 Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5): 485-489, 514 doi: 10.3321/j.issn:1001-0920.2004.05.002
[141]	王飞跃.软件定义的系统与知识自动化:从牛顿到默顿的平行升华.自动化学报, 2015, 41(1): 1-8 doi: 10.3969/j.issn.1003-8930.2015.01.001 Wang Fei-Yue. Software-defined systems and knowledge automation: a parallel paradigm shift from Newton to Merton. Acta Automatica Sinica, 2015, 41(1): 1-8 doi: 10.3969/j.issn.1003-8930.2015.01.001
[142]	Zheng N N, Liu Z Y, Ren P J, Ma S T, Yu S Y, Xue J R, et al. Hybrid-augmented intelligence: collaboration and cognition. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 153-179 http://d.old.wanfangdata.com.cn/Periodical/zjdxxbc-e201702002
[143]	Zhang B, Zhang L. Multi-granular computing in web age. In: Proceedings of the 14th International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. Berlin, Heidelberg: Springer, 2013. 11-14