基于迁移学习的数据流分类研究综述
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摘要
数据流分类作为数据挖掘领域中的一个重要分支,能够获取数据流中有价值的信息,已成为当下研究热点之一.由于数据流固有特性导致传统的数据流分类方法面临较多难题,如样本标注和概念漂移等.本文分析了增量式和集成式的数据流传统分类方法的优缺点,在此基础上阐述迁移学习在数据流分类中的可行性和当前的研究进展,归纳出基于迁移学习的数据流分类研究的主要关键问题,指出进一步研究方向.
As an important branch in the field of data mining,data stream classification can obtain valuable information in data streams,which has become one of the research hotspots. Due to the inherent characteristics of data streams,traditional data stream classification methods face more problems,such as sample labeling and concept drift. This paper analyzes the advantages and disadvantages of the traditional classification method of incremental and integrated data streams. On the basis of this,this paper expounds the feasibility and current research progress of transfer learning in data stream classification,summarizes the main key problems of data stream classification based on transfer learning,and points out the further research direction.
As an important branch in the field of data mining,data stream classification can obtain valuable information in data streams,which has become one of the research hotspots. Due to the inherent characteristics of data streams,traditional data stream classification methods face more problems,such as sample labeling and concept drift. This paper analyzes the advantages and disadvantages of the traditional classification method of incremental and integrated data streams. On the basis of this,this paper expounds the feasibility and current research progress of transfer learning in data stream classification,summarizes the main key problems of data stream classification based on transfer learning,and points out the further research direction.
引文
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[34]Wu Q,Wu H,Zhou X,et al.Online transfer learning with multiple homogeneous or heterogeneous sources[J].IEEETransactions on Knowledge and Data Engineering,2017,29(7):1494-1507.
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[36]Freund Y,Schapire R E.A decision-theoretic generalization of online learning and an application to boosting[J].Journal of computer and system sciences,1997,55(1):119-139.
[2]文益民,强保华,范志刚.概念漂移数据流分类研究综述[J].智能系统学报,2013,8(2):95-104.
[3]庄福振,罗平,何清,等.迁移学习研究进展[J].软件学报,2015,26(1):26-39.
[4]唐诗淇,文益民,秦一休,等.一种基于局部分类精度的多源在线迁移学习算法[J].软件学报,2017,28(11):2940-2960.
[5]文益民,唐诗淇,冯超,等.基于在线迁移学习的重现概念漂移数据流分类[J].计算机研究与发展,2016,53(8):1781-1791.
[6]Feng Z,Wang M,Yang S,et al.Incremental semisupervised classification of data streams via selfrepresentative selection[J].Applied Soft Computing,2016,47:389-394.
[7]Xu S,Wang J.A fast incremental extreme learning machine algorithm for data streams classification[J].Expert Systems with Applications,2016,65:332-344.
[8]Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1):489-501.
[9]Huang G B,Zhou H,Ding X,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Trans Syst Man Cybern B Cybern,2012,42(2):513-529.
[10]Liang N,Huang G,Saratchandran P,et al.A fast and accurate online sequential learning algorithm for feedforward networks[J].IEEE Transactions on Neural Networks,2006,17(6):1411-23.
[11]Loo H R,Marsono M N.Online data stream classification with incremental semi-supervised learning[C]//Proceedings of the Second ACM IKDD Conference on Data Sciences.New York,USA:ACM,2015:132-133.
[12]Elwell R,Polikar R.Incremental learning of concept drift in nonstationary environments[J].IEEE Transactions on Neural Networks,2011,22(10):1517-1531.
[13]Lu Y,Cheung Y,Tang Y Y.Dynamic weighted majority for incremental learning of imbalanced data streams with concept drift[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.Melbourne,Australia:AAAI Press,2017:2393-2399.
[14]Street W N,Kim Y S.A streaming ensemble algorithm(SEA)for largescale classification[C]//Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining.San Francisco,Ca,USA:ACM,2001:377-382.
[15]Salzberg S L.C4.5:Programs for machine learning[J].Machine Learning,1994,16(3):235-240.
[16]Wang H,Fan W,Yu P S,et al.Mining concept-drifting data streams using ensemble classifiers[C]//Proceedings of the ninth ACM SIGKDD international conference on Knowledge di scovery and data mining.Washington,DC,USA:ACM,2003:226-235.
[17]Hosseini M J,Gholipour A,Beigy H.An ensemble of clusterbased classifiers for semisupervised classification of nonstationary data streams[J].Knowledge&Informa tion Systems,2016,46(3):567-597.
[18]Abbaszadeh O,Amiri A,Khanteymoori A R.An ensemble method for data stream classification in the presence of concept drift[J].Frontiers of Information Technology&Electronic Engineering,2015,16(12):1059-1068.
[19]Yang R,Xu S,Feng L.An ensemble extreme learning machine for data stream classification[J].Algorithms,2018,11(7):107.
[20]Liang N Y,Huang G B,Saratchandran P,et al.A fast and accurate online sequential learning algorithm for feedf orward networks[J].IEEE Transactions on Neural Networks,2006,17(6):1411-1423.
[21]Krawczyk B,Cano A.Online ensemble learning with abstaining classifiers for drifting and noisy data streams[J].Applied Soft Computing,2018,68:677-692.
[22]Krawczyk B.Active and adaptive ensemble learning for online activity recognition from data streams[J].Knowledge-Based Systems,2017,138:69-78.
[23]Pan S J,Yang Q.A survey on transfer learning[J].IEEETransactions on Knowledge&Data Engineering,2010,22-(10):1345-1359.
[24]Dai W,Yang Q,Xue G R,et al.Boosting for transfer learning[C]//International Conference on Machine Learning.Cincinnati,Ohio,USA:ACM,2007:193-200.
[25]Ge L,Gao J,Zhang A.OMS-TL:A framework of online multiple source transfer learning[C]//Proceedings of the22nd ACM Internation Conference on Information and Knowledge Management,San Francisco,California,USA:ACM,2013:2023-2028.
[26]Wu Q,Zhou X,Yan Y,et al.Online transfer learning by leveraging multiple source domains[J].Knowledge&Information Systems,2017,52(3):1-21.
[27]Crammer K,Dekel O,Keshet J,et al.Online passiveaggressive algorithms[J].Journal of Machine Learning Research,2006,7(3):551-585.
[28]Zhao P,Hoi S C,Wang J,et al.Online transfer learning[J].Artificial Intelligence,2014,216:76-102.
[29]Zhao P,Hoi S C.OTL:A framework of online transfer learning[C]//Proceedings of the 27th international conference on machine learning(ICML-10).Haifa,Israel:ACM,2010:1231-1238.
[30]Bhatt H S,Rajkumar A,Roy S.Multi-source iterative adaptation for cross-domain classi覱cation[C]//Proceedings of International Joint Conference on Artificial Intelligence.New York,USA:AAAI/IJCAI,2016:3691-3697.
[31]Pan S J,Tsang I W,Kw ok J T,et al.Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks,2011,22(2):199-210.
[32]Wang J,Zhao P,Hoi S C H,et al.Online feature selection and its applications[J].IEEE Transactions on Knowledge&Data Engineering,2014,26(3):698-710.
[33]Zhang X,Zhuang Y,Wang W,et al.Online feature transformation learning for crossdomain object category Recognition[J].IEEE transactions on neural networks and learning systems,2018,29(7):2857-2871.
[34]Wu Q,Wu H,Zhou X,et al.Online transfer learning with multiple homogeneous or heterogeneous sources[J].IEEETransactions on Knowledge and Data Engineering,2017,29(7):1494-1507.
[35]Yan Y,Wu Q,Tan M,et al.Online heterogeneous transfer learning by weighted offline and online classifiers[C]//European Conference on Computer Vision.Amsterdam,The Netherlands:Springer,2016:467-474.
[36]Freund Y,Schapire R E.A decision-theoretic generalization of online learning and an application to boosting[J].Journal of computer and system sciences,1997,55(1):119-139.