大数据智能决策

于洪; 何德牛; 王国胤; 李劼; 谢永芳; 于洪; 何德牛; 王国胤; 李劼; 谢永芳

doi:10.16383/j.aas.c180861

[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