数据流概念漂移处理方法研究综述

doi:10.11896/jsjkx.210700112

摘要/Abstract

摘要： 目前非稳态数据流中的概念漂移愈来愈呈现出不同速度、不同空间分布的趋势,给数据挖掘、机器学习等诸多领域带来了极大的挑战。近二十年来,许多致力于在非稳态数据流中处理概念漂移的技术方法被提出。从一种新颖的角度,分别针对主动检测的显式方法和被动自适应的隐式方法对目前的概念漂移处理技术方法进行了全面的阐述。首先,从处理某一特定类型和多种类型的概念漂移的角度对主动检测方法进行了分析,并从单学习器和集成学习的角度对被动自适应方法进行了分析;其次,对诸多概念漂移处理方法的对比算法、学习模型、适用漂移类型、算法的优缺点进行了全面总结;最后给出了未来的研究方向,包括类不平衡的数据流概念漂移处理方法、含新颖类的概念漂移数据流处理方法、含噪声的数据流概念漂移处理方法等方面。

关键词: 数据流, 概念漂移, 分类, 主动方法, 被动方法

Abstract: At present,concept drift in the nonstationary data stream presents a trend of different speeds and and different space distribution,which has brought great challenges to many fields such as data mining and machine learning.In the past two de-cades,many methods dedicated to handling concept drift in nonstationary data streamsemerged.A novel perspective is proposed to classify these methods.The current concept drift handling methods are comprehensively explained from the explicit method of actively detection and the implicit method of passively adaption.In particular,active detection methods are analyzed from the per-spective of handling one specific type of concept drift and handling multiple types of concept drift,and passive adaptive methods are analyzed from the perspectives of single learner and ensemble learning.Many concept drift handling methods are analyzed and summarized in terms of the comparison algorithm,learning model,applicable drift type,advantages and disadvantages of algorithms.Finally,further research directions are given,including the concept drift handling methods in class-imbalanced data streams,the concept drift handling methods in data stream with the existence of novel classes,and the concept drift handling methods in the data stream with noise.

Key words: Data stream, Concept drift, Classification, Active methods, Passive method

中图分类号:

TP391

陈志强, 韩萌, 李慕航, 武红鑫, 张喜龙. 数据流概念漂移处理方法研究综述[J]. 计算机科学, 2022, 49(9): 14-32. https://doi.org/10.11896/jsjkx.210700112

CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long. Survey of Concept Drift Handling Methods in Data Streams[J]. Computer Science, 2022, 49(9): 14-32. https://doi.org/10.11896/jsjkx.210700112

参考文献

[1]DONGRE P B,MALIK L G.A review on real time data stream classification and adapting to various concept drift scenarios[C]//Proceedings of 2014 IEEE International Advance Computing Conference(IACC).Gurgaon:IEEE,2014:533-537.
[2]MASSI MC,IEVA F,LETTIERI E.Data mining application to healthcare fraud detection:a two-step unsupervised clustering method for outlier detection with administrative databases[J].BMC Medical Informatics and Decision Making,2020,20(1):1-11.
[3]FDEZ-RIVEROLA F,IGLESIAS E,DIAZ F,et al.Applying lazy learning algorithms to tackle concept drift in spam filtering[J].Expert Systems with Applications,2007,33(1):36-48.
[4]AHMIM A,GHOUALMI-ZINE N.A new adaptive intrusiondetection system based on the intersection of two different classifiers[J].International Journal of Security and Networks,2014,9(3):125-132.
[5]DEMERTZIS K,ILIADIS L,ANEZAKIS V.A Dynamic En-semble Learning Framework for Data Stream Analysis and Real-Time Threat Detection[C]//Proceedings of Artificial Neural Networks and Machine Learning(ICANN).Greece:Sprin-ger,2018:669-681.
[6]DASH R,SAMAL S,DASH R,et al.An integrated TOPSIS crow search based classifier ensemble:In application to stock index price movement prediction[J].Applied Soft Computing,2019,85:105784.
[7]DU S Y,HAN M,SHEN M Y,et al.Survey of Ensemble Classification Algorithms for Data Streams with Concept Drift[J].Computer Engineering,2020,46(1):15-24.
[8]IWASHITA A S,PAPA J P.An Overview on Concept Drift Learning[J].IEEE Access,2018,7:1532-1547.
[9]GAMA J,MEDAS P.Learning with Drift Detection[J].Ad-vances in Artificial Intelligence-SBIA,2004,3171:286-295.
[10]BAENA-GARC M,CAMPO-ÁVILA J D,FIDALGO-MERINO R,et al.Early drift detection method[J].International Workshop on Knowledge Discovery from Data Streams,2006,6:77-86.
[11]BIFET A,GAVALDÁ R.Learning from Time-Changing Datawith Adaptive Windowing[C]//Proceedings of the Seventh SIAM International Conference on Data Mining.Minneapolis:SIAM,2007:443-448.
[12]NISHIDA K,YAMAUCHI K,OMORI T.ACE:Adaptive Classifiers-Ensemble System for Concept-Drifting Environments[C]//Proceedings of International Conference on Multiple Classifier Systems.Verlag:Springer,2005,176-185.
[13]GOMES H M,BIFET A,READ J,et al.Adaptive random forests for evolving data stream classification[J].Machine Lear-ning,2017,106(9/10):1469-1495.
[14]HULTEN G,SPENCER L,DOMINGOS P.Mining Time-Changing Data Streams[C]//Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2001:97-106.
[15]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):1411-1423.
[16]STREET W N,KIM Y S.A streaming ensemble algorithm(SEA)for large-scale classification[C]//Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery & Data Mining.New York:ACM,2001:377-382.
[17]WANG H,YU P S,HAN J.Mining Concept-Drifting DataStreams[M]//Data Mining & Knowledge Discovery Handbook.2003:789-802.
[18]KOLTER J Z,MALOOF M A.Dynamic weighted majority:anew ensemble method for tracking concept drift[C]//Procee-dings of the Third International IEEE Conference on Data Mining.Los Alamitos:IEEE Computer Society,2003:123-130.
[19]WARES S,ISAACS J,ELYAN E.Data stream mining:methods and challenges for handling concept drift[J].SN Applied Sciences,2019,1(11):1412.
[20]RAMIREZ-GALLEGO S,KRAWCZYK B,GARCIA S,et al.A survey on data preprocessing for data stream mining:Current status and future directions[J].Neurocomputing,2017,239:39-57.
[21]GAMA J,LIOBAIT I,BIFET A,et al.A Survey on Concept Drift Adaptation[J].ACM Computing Surveys,2014,46(4):4410-4437.
[22]KHAMASSI I,SAYED-MOUCHAWEH M,HAMMAMI M,et al.Discussion and review on evolving data streams and concept drift adapting[J].Evolving Systems,2016,9(1):1-23.
[23]LU J,LIU A,DONG F,et al.Learning under Concept Drift:A Review[J].IEEE Transactions on Knowledge and Data Engineering,2020,31(12):2346-2363.
[24]MINKU L,WHITE A,XIN Y.The Impact of Diversity on On-line Ensemble Learning in the Presence of Concept Drift[J].IEEE,2010,22(5):730-742.
[25]TSYMBAL A.The Problem of Concept Drift:Definitions and Related Work[J/OL].Journal of Information Security.https://www.researchgate.net/publication/228723141.
[26]DELANY S J,CUNNINGHAM P.A case-based technique for tracking concept drift in spam filtering[J].Knowledge-Based Systems,2004,18(4/5):187-195.
[27]KRAWCZYK B,MINKU L L,GAMA J,et al.Ensemble lear-ning for data stream analysis:A survey[J].Information Fusion,2017,37:132-156.
[28]OLIVEIRA G H F M,MINKU L L,OLIVEIRA A L I.GMM-VRD:A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts[C]//Proceedings of International Joint Conference on Neural Networks(IJCNN).Budapest:IEEE,2019:1-8.
[29]TSYMBAL A,PECHENIZKIY M,CUNNINGHAM P,et al.Handling Local Concept Drift with Dynamic Integration of Classifiers:Domain of Antibiotic Resistance in Nosocomial Infections[C]//Proceedings of 19th IEEE International Symposium on Computer-Based Medical Systems(CBMS).Salt Lake City:IEEE,2006:679-684.
[30]KHAMASSI I,SAYED-MOUCHAWEH M,HAMMAMI M,et al.Self-Adaptive Windowing Approach for Handling Complex Concept Drift[J].Cognitive Computation,2015,7(6):772-790.
[31]ŽLIOBAITE I,BUDKA M,STAHL F.Towards cost-sensitive adaptation:When is it worth updating your predictive model[J].Neurocomputing,2015,150:240-249.
[32]REN S,LIAO B,ZHU W,et al.Knowledge-maximized ensemble algorithm for different types of concept drift[J].Information Sciences,2018,430/431:261-281.
[33]HU H,KANTARDZIC M,SETHI T S.No Free Lunch Theorem for concept drift detection in streaming data classification:A review[J/OL].Wiley Interdisciplinary Reviews:Data Mining and KnowledgeDiscovery,https://doi.org/10.1002/widm.1327.
[34]NISHIDA K,YAMAUCHI K.Detecting Concept Drift UsingStatistical Testing[M].Berlin:Springer:264-269.
[35]MAHDI O A,PARDEDE E,ALI N,et al.Diversity measure asa new drift detection method in data streaming[J].Knowledge-Based Systems,2020,191:105227.
[36]HUANG D T J,KOH Y S,DOBBIE G,et al.Detecting Volatility Shift in Data Streams[C]//Proceedings of IEEE International Conference on Data Mining(ICDM).Shenzhen:IEEE,2014:863-868.
[37]PESARANGHADER A,VIKTOR H L.Fast Hoeffding DriftDetection Method for Evolving Data Streams[C]//Proceedings of Joint European Conference on Machine Learning and Know-ledge Discovery in Databases.2016:96-111.
[38]PESARANGHADER A,VIKTOR H L.McDiarmid Drift Detection Methods for Evolving Data Streams[C]//Proceedings of 2018 International Joint Conference on Neural Networks.2018:1-9.
[39]MINKU L L,YAO X.DDD:A New Ensemble Approach forDealing with Concept Drift[J].IEEE transactions on Knowledge and Data Engineering,2012,24(4):619-633.
[40]SANTOS S G T D,GONÇALVES P M,SILVA G D D S,et al.Speeding Up Recovery from Concept Drifts[C]//Proceedings of Machine Learning and Knowledge Discovery in Databases-European Conference.Nancy:Springer,2014:179-194.
[41]BARROS R S M D,GARRIDO T.DE CARVALHO SANTOS S,et al.A Boosting-like Online Learning Ensemble[C]//International Joint Conference on Neural Networks(IJCNN).Vancouver:IEEE,2016:1871-1878.
[42]SUN Y,WANG Z,LIU H,et al.Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift[J].International Journal of Distributed Sensor Networks,2016,12(5):4218973.
[43]FRIAS-BLANCO I,CAMPO-AVILA J D,RAMOS-JIMENEZG,et al.Online and Non-Parametric Drift Detection Methods Based on Hoeffding Bounds[J].IEEE Transactions on Know-ledge and Data Engineering,2015,27(3):810-823.
[44]OZA N C.Online Bagging and Boosting[C]//Proceedings of 2005 IEEE International Conference on Systems,Man and Cybernetics.Waikoloa:IEEE,2005:2340-2345.
[45]GAMA J,KOSINA P.Tracking Recurring Concepts with Meta-learners[C]//Proceedings of 14th Portuguese Conference on Artificial Intelligence.Aveiro:Springer,2009:423-434.
[46]GAMA J,KOSINA P.Recurrent concepts in data streams classification[J].Knowledge and Information Systems,2014,40(3):489-507.
[47]ANGEL A,JOĀO BÁRTOLO G,ERNESTINA M.Predictingrecurring concepts on data-streams by means of a meta-model and a fuzzy similarity function[J].Expert Systems with Applications,2016,46:87-105.
[48]ANDERSON R,KOH Y S,DOBBIE G.CPF:Concept Profiling Framework for Recurring Drifts in Data Streams[M].Cham:Springer International Publishing,2016:203-214.
[49]ANDERSON R,KOH Y S,DOBBIE G,et al.Recurring Concept Meta-learning for Evolving Data Streams[J].Expert Systems with Applications,2019,138:112832.
[50]GONÇALVES J P M,BARROS R S M D.RCD:A recurringconcept drift framework[J].Pattern Recognition Letters,2013,34(9):1018-1025.
[51]SAKTHITHASAN S,PEARS R,BIFET A,et al.Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams[C]//Proceedings of International Joint Conference on Neural Networks.Killarney:IEEE,2015:1-8.
[52]SRIPIRAKAS S,PEARS R.Mining Recurrent Concepts in Data Streams Using the Discrete Fourier Transform[J].Data Warehousing and Knowledge Discovery,2014,8646:439-451.
[53]SIDHU P,BHATIA M P S.A two ensemble system to handle concept drifting data streams:recurring dynamic weighted majority[J].International Journal of Machine Learning and Cybernetics,2019,10(3):563-578.
[54]GOMES J B,GABER M M,SOUSA P A C,et al.Mining Recurring Concepts in a Dynamic Feature Space[J].IEEE Transaction on Neural Networks and Learning Systems,2014,25(1):95-110.
[55]HU J,CHEN J,QIN X.Algorithm of Recurring Concept Drift Base on Main Feature Extraction,2019[C]//Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence(ICCAI).Bali:ACM,2019:59-65.
[56]ZHENG X,LI P,HU X,et al.Semi-supervised classification on data streams with recurring concept drift and concept evolution[J].Knowledge-Based Systems,2021,215:106749.
[57]ALIPPI C,BORACCHI G,ROVERI M.An effective just-in-time adaptive classifier for gradual concept drifts[C]//Procee-dings of the 2011 International Joint Conference on Neural Networks(IJCNN).IEEE,2011:1675-1682.
[58]LIU A,ZHANG G,JIE L.Fuzzy Time Windowing for Gradual Concept Drift Adaptation[C]//Proceedings of 2017 IEEE International Conference on Fuzzy Systems(FUZZ-IEEE).Naples:IEEE,2017:1-6.
[59]ABDUALRHMAN M A A,PADMA M C.Deterministic Concept Drift Detection in Ensemble Classifier Based Data Stream Classification Process[J].International Journal of Grid and High Performance Computing,2019,11(1):29-48.
[60]TSYMBAL A,PECHENIZKIY M,CUNNINGHAM P,et al.Dynamic integration of classifiers for handling concept drift[J].Information Fusion,2008,9(1):56-68.
[61]GAMA J,CASTILLO G.Learning with Local Drift Detection[C]//Proceedings of Advanced Data Mining and Applications,Second International Conference(ADMA).Xi’an:Springer,2006:42-55.
[62]IKONOMOVSKA E,GAMA J,SEBASTIAO R,et al.Regression trees from data streams with drift detection[C]//Procee-dings of Discovery Science,12th International Conference.Porto:Springer,2009:121-135.
[63]SHAKER A,LUGHOFER E.Self-adaptive and local strategies for a smooth treatment of drifts in data streams[J].EvolvingSystems,2014,5(4):239-257.
[64]LIU A,SONG F,ZHANG G,et al.Regional Concept Drift Detection and Density Synchronized Drift Adaptation[C]//Proceedings of Twenty-Sixth International Joint Conference on Artificial Intelligence.2017:2280-2286.
[65]LIU A J,LU J.Accumulating regional density dissimilarity for concept drift detection in data streams[J].Pattern Recognition,2018(76):256-272.
[66]LIU A,LU J,ZHANG G.Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation[J].IEEE Transactions on Neural Networks and Learning Systems,2021,32(1):293-307.
[67]OLIVEIRA L S,BATISTA G E.Igmm-cd:a gaussian mixture classification algorithm for data streams with concept drifts[C]//Proceedings of BRACIS,2015 Brazilian Conference on Intelligent Systems(BRACIS).IEEE,2015:55-61.
[68]ALMEIDA P R,OLIVEIRA L S,BRITTO A S,et al.Adapting dynamic classifier selection for concept drift[J].Expert Systems with Applications,2018,104:67-85.
[69]GZÜAK M,CAN F.Concept learning using one-class classifiers for implicit drift detection in evolving data streams[J].Artificial Intelligence Review,2020(3):1-23.
[70]YAN M M W.Accurate detecting concept drift in evolving data streams[J].ICT Express,2020,6(4):332-338.
[71]MICEVSKA S,AWAD A,SAKR S.SDDM:an interpretablestatistical concept drift detection method for data streams[J].Journal of Intelligent Information Systems,2021,56:459-484.
[72]MUSEBA T,NELWAMONDO F,OUAHADA K,et al.Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts[J].Applied Computational Intelligence and Soft Computing,2021,13:5533777.
[73]GOEL K,BATRA S.Dynamically updated diversified ensemble-based approach for handling concept drift[J].Turkish Journal of Electrical Engineering & Computer Sciences,2020,28(1):556-574.
[74]REIS D,FLACH P,MATWIN S,et al.Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Francisco:ACM,2016:1545-1554.
[75]VERDECIA-CABRERA A,BLANCO I F,CARVALHO A C P L.An online adaptive classifier ensemble for mining non-stationa-ry data streams[J].Intelligent Data Analysis,2018,22(4):787-806.
[76]DONG F,LU J,ZHANG G,et al.Active Fuzzy Weighting Ensemble for Dealing with Concept Drift[J].International Journal of Computational Intelligence Systems,2018,11(1):438.
[77]ZHANG B,CHEN Y,XUE L.Research on Concept-DriftingData Stream Based on Fuzzy Integral Ensemble Classifier System[C]//Proceedings of Communications,Signal Processing,and Systems.Singapore:Springer,2019:225-232.
[78]MONTIEL J,MITCHELL R,FRANK E,et al.Adaptive XG-Boost for Evolving Data Streams[C]//Proceedings of 2020 International Joint Conference on Neural Networks(IJCNN).Glasgow:IEEE,2020:1-8.
[79]ABDULSALAM H,SKILLICORN D B,MARTIN P.Streaming Random Forests[C]//Proceedings of Eleventh International Database Engineering and Applications Symposium(IDEAS 2007).Banff:IEEE Computer Society,2007:22-36.
[80]ABDULSALAM H,SKILLICORN D B,MARTIN P.Classif-ying Evolving Data Streams UsingDynamic Streaming Random Forests[C]//Proceedings of Database and Expert Systems Applications,19th International Conference.Turin:Springer,2008:643-651.
[81]GOMES H M,BIFET A,READ J,et al.Adaptive random fo-rests for evolving data stream classification[J].Machine Lear-ning,2017,106(9/10):1469-1495.
[82]HO T K.The random subspace method for constructing decision forests[J].IEEE transactions on Pattern Analysisand Machine Intelligence,1998,20(8):832-844.
[83]LUONG A V,NGUYEN T T,LIEW A W.Streaming Active Deep Forest for Evolving Data Stream Classification[J].arXiv:2002.11816,2020.
[84]ZHOU Z H,FENG J.Deep Forest:Towards An Alternative to Deep Neural Networks[C]//Proceedings of Twenty-Sixth International Joint Conference on Artificial Intelligence.Melbourne:ijcai,2017:3553-3559.
[85]HAQUE A,KHAN L,BARON M,et al.SAND:semi-super-vised adaptive novel class detection and classification over data stream[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.Phoenix:AAAI Press,2016:1652-1658.
[86]HAQUE A,KHAN L,BARON M,et al.Efficient handling of concept drift and concept evolution over Stream Data[C]//Proceedings of Efficient Handling of Concept Drift and Concept Evolution Over Stream Data.Helsinki:IEEE,2016:481-492.
[87]ZLIOBAITE I.Change with Delayed Labeling:When is it Detectable?[C]//Proceedings of The 10th IEEE International Conference on Data Mining Workshops.Sydney:IEEE,2010:843-850.
[88]RAAB C,HEUSINGER M,SCHLEIF F.Reactive Soft Prototype Computing for Concept Drift Streams[J].Neurocompu-ting,2020,416:340-351.
[89]YUAN Y,WANG Z,WANG W.Unsupervised concept drift detection based on multi-scale slide windows[J].Ad Hoc Networks,2021,111:102325.
[90]DU L,SONG Q,ZHU L,et al.A Selective Detector Ensemble for Concept Drift Detection[J].The Computer Journal,2015,58(3):457-471.
[91]HU H,KANTARDZIC M,LYU L.Detecting Different Types of Concept Drifts with Ensemble Framework[C]//Proceedings of 17th IEEE International Conference on Machine Learning and Applications.Orlando:IEEE,2018:344-350.
[92]KORYCKI L,KRAWCZYK B.Unsupervised Drift Detector Ensembles for Data Stream Mining[C]//Proceedings of 2019 IEEE International Conference on Data Science and Advanced Analy-tics.Washington:IEEE,2019:317-325.
[93]LI Y,LI D,WANG S,et al.Incremental entropy-based clustering on categorical data streams with concept drift[J].Know-ledge-Based Systems,2014,59(1):33-47.
[94]BAI L,CHENG X,LIANG J,et al.An optimization model for clustering categorical data streams with drifting concepts[J].IEEE Trans.Knowl.Data Eng.2016,28(11):2871-2883.
[95]KATAKIS I,TSOUMAKAS G,VLAHAVAS I.Tracking recurring contexts using ensemble classifiers:an application to email filtering[J].Knowledge and Information Systems,2010,22(3):371-391.
[96]XU S,FENG L,LIU S,et al.Self-adaption neighborhood density clustering method for mixed data stream with concept drift[J].Engineering Applications of Artificial Intelligence,2020,89:103451.
[97]MUSEBA T,NELWAMONDO F,OUAHADA K.ADES:ANew Ensemble Diversity-Based Approach for Handling Concept Drift[J].Mobile Information Systems,2021,2021(4):1-17.
[98]DOMINGOS P,HULTEN G.Mining high-speed data streams,2000[C]//Proceedings of the sixth ACM SIGKDD International Conferenceon Knowledge Discovery and Data Mining.Boston:ACM,2000:71-80.
[99]BLAKE C,NTOUTSI E.Reinforcement Learning Based Decision Tree Induction Over Data Streams with Concept Drifts,2018[C]//Proceedings of Reinforcement Learning Based Decision Tree Induction Over Data Streams with Concept Drifts.Singapore:IEEE,2018:328-335.
[100]JANKOWSKI D,JACKOWSKI K.An Increment Decision Tree Algorithm for Streamed Data[C]//Proceedings of 2015 IEEE TrustCom/BigDataSE/ISPA.Helsinki:IEEE,2015:199-204.
[101]JANKOWSKI D,JACKOWSKI K,CYGANEK B.Learning Decision Trees from Data Streams with Concept Drift[J].Procedia Computer Science,2016,80:1682-1691.
[102]COSTA T,GUILHERME V.Strict Very Fast Decision Tree:a memory conservative algorithm for data stream mining[J].Pattern Recognition Letters,2018,116(1):22-28.
[103]BIFET A,ZHANG J,FAN W,et al.Extremely Fast Decision Tree Mining for Evolving Data Streams[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Halifax:ACM,2017:1733-1742.
[104]KRAWCZYK B,WOZNIAK M.Weighted Naive Bayes Classi-fier with Forgetting for Drifting Data Streams[C]//Proceedings of 2015 IEEE International Conference on Systems,Man,and Cybernetics(SMC).Hong Kong:IEEE,2015:2147-2152.
[105]ZHAO Q,KLAUE C,LAI C.Predicting concept drift viadynamic NaÏve Bayes[C]//Proceedings of 2017 IEEE International Conference on Big Data.Boston:IEEE,2017:2420-2425.
[106]LIU A,ZHANG G.Fast Switch NaÏve Bayes to Avoid Redundant Update for Concept Drift Learning[C]//Proceedings of 2020 International Joint Conference on Neural Networks(IJCNN).Glasgow:IEEE,2020:1-7.
[107]KISHORE B D,RAMADEVI Y,RAMANA K V.RGNBC:Rough Gaussian NaÏve Bayes Classifier for Data Stream Classification with Recurring Concept Drift[J].Arabian Journal for Science and Engineering,2016,42(2):705-714.
[108]BABU D K,RAMADEVI Y,RAMANA K V.PGNBC:Pearson Gaussian NaÏve Bayes classifier for data stream classification with recurring concept drift[J].Intelligent Data Analysis,2017,21(5):1173-1191.
[109]SYED N,LIU H,SUNG K.Handling concept drifts in incremental learning with support vector machines[C]//Proceedings of the Fifth ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining.San Diego:ACM,1999:317-321.
[110]RUPING S.Incremental Learning with Support Vector Ma-chines[C]//Proceedings of the 2001 IEEE International Conference on Data Mining.San Jose:IEEE,2001:641-642.
[111]YALCIN A,ERDEM Z,GURGEN F.Ensemble based incremental SVM classifiers for changing environments[C]//Proceedings of 2007 22nd International Symposium on Computer and Information Sciences.Ankara:IEEE,2007.
[112]AYAD O.Learning under Concept Drift with Support Vector Machines[C]//Proceedings of 24th International Conference on Artificial Neural Networks.Hamburg:Springer,2014:587-594.
[113]ZAREMOODIP,SIAHROUDI S K,BEIGY H.A support vector based approach for classification beyond the learned label space in data streams[C]//Proceedings of the 31st Annual ACM Symposium on Applied Computing.Pisa:ACM,2016:910-915.
[114]ZHU Q Y,SIEW C K.Extreme Learning Machine:A NewLearning Scheme of Feedforward Neural Networks[J].Neurocomputing,2006,70:489-501.
[115]LIANG N,HUANG G,SARATCHANDRAN P,et al.A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks[J].IEEE Transactionson Neural Networks,2006,17(6):1411-1423.
[116]XU S L,B J W A.A fast incremental extreme learning machine algorithm for data streams classification[J].Expert Systems with Applications,2016,65:332-344.
[117]ZHAI J,WANG J G.Ensemble online sequential extremelearning machine for large data set classification[C]//Procee-dings of 2014 IEEE International Conference on Systems,Man,and Cybernetics(SMC).San Diego:IEEE,2014:2250-2255.
[118]LAN Y,SOH Y C,HUANG G.Ensemble of online sequential extreme learning machine[J].Neurocomputing,2009,72(13/14/15):3391-3395.
[119]XU S,WANG J.Dynamic extreme learning machine for data stream classification[J].Neurocomputing,2017,238:433-449.
[120]LIU D,WU Y X,JIANG H.FP-ELM:An online sequential learning algorithm for dealing with concept drift[J].Neurocomputing,2016,207:322-334.
[121]DA SILVA C A S,KROHLING R A.Semi-Supervised Online Elastic Extreme Learning Machine with Forgetting Parameter to deal with concept drift in data streams[C]//Proceedings of International Joint Conference on Neural Networks.Budapest:IEEE,2019:1-8.
[122]PRATAMA M,LU J,LUGHOFER E,et al.An Incremental Learning of Concept Drifts Using Evolving Type-2 Recurrent Fuzzy Neural Networks[J].IEEE Transactions on Fuzzy Systems,2017,25(5):1175-1192.
[123]LOBO J L,BAI L,JAVIER D S,et al.Evolving Spiking Neural Networks for online learning over drifting data streams[J].Neural Networks,2018,108:1-19.
[124]ANDRADE S J D,HRUSCHKA E R,GAMA J.An evolutiona-ry algorithm for clustering data streams with a variable number of clusters[J].Expert Systems with Applications,2017,67:228-238.
[125]BAHRI M,MANIU S,BIFET A.A Sketch-Based Naive Bayes Algorithms for Evolving Data Streams[C]//Proceedings of 2018 IEEE International Conference on Big Data.Seattle:IEEE,2018:604-613.
[126]LOSING V,HAMMER B,WERSING H.KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift,2016[C]//Proceedings of IEEE 16th International Conference on Data Mining.Barcelona:IEEE,2016:291-300.
[127]TENNANT M,STAHL F,RANA O,et al.Scalable real-time classification of data streams with concept drift[J].Future Gene-ration Computer Systems,2017,75:187-199.
[128]BRZEZINSKI D,STEFANOWSKI J.Accuracy Updated Ensemble for Data Streams with Concept Drift[C]//Proceedings of International Conference on Hybrid Artificial Intelligent Systems.Wroclaw:Springer,2011:153-160.
[129]BRZEZINSKI D,STEFANOWSKI J.Reacting to DifferentTypes of Concept Drift:The Accuracy Updated Ensemble Algorithm[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(1):81-94.
[130]ELWELL R,POLIKAR R.Incremental Learning of ConceptDrift in Nonstationary Environments[J].IEEE Transactions on Neural Networks,2011,22(10):1517-1531.
[131]LI Z,HUANG W,XIONG Y,et al.Incremental learning imba-lanceddata streams with concept drift:The dynamic updated ensemble algorithm[J].Knowledge-Based Systems,2020,195:105694.
[132]KLIKOWSKI J,WOZ＇NIAK M.Multi Sampling Random Subspace Ensemble for Imbalanced Data Stream Classification[C]//Proceedings of International Conference on Computer Recognition Systems.Polanica-Zdrój:Springer,2019:360-369.
[133]WOZNIAK M,KSIENIEWICZ P,CYGANEK B,et al.ActiveLearning Classification of Drifted Streaming Data[J].Procedia Computer Science,2016,80(C):1724-1733.
[134]JACKOWSKI K.New diversity measure for data stream classification ensembles[J].Engineering Applications of Artificial Intelligence,2018,74:23-34.
[135]BERTINI J J R,NICOLETTI M D C.An iterative boosting-based ensemble for streaming data classification[J].Information Fusion,2019,45:66-78.
[136]REN S,LIAO B,ZHU W,et al.The Gradual Resampling Ensemble for mining imbalanced data streams with concept drift[J].Neurocomputing,2018,286:150-166.
[137]DUDA P.On Ensemble Components Selection in Data Streams Scenario with Gradual Concept-Drift[C]//Proceedings of 17th Artificial Intelligence and Soft Computing International Confe-rence.Zakopane:Springer,2018:311-320.
[138]BERTINI J J R.A Discretization-based Ensemble LearningMethod for Classification in High-Speed Data Streams[C]//Proceedings of International Joint Conference on Neural Networks.Budapest:IEEE,2019:1-8.
[139]BACH S H,MALOOF M A.Paired Learners for Concept Drift[C]//Proceedings of the 8th IEEE International Conference on Data Mining.Pisa:IEEE,2008:23-32.
[140]BRZEZINSKI D,STEFANOWSKI J.Combining block-basedand online methods in learning ensembles from concept drifting data streams[J].Information Sciences,2014,265:50-67.
[141]JABER G,CORNUÉJOLS A,TARROUX P.A New On-Line Learning Method for Coping with Recurring Concepts:The ADACC System[C]//Proceedings of Neural Information Processing-20th International Conference.Daegu:Springer,2013:595-604.
[142]KRAWCZYK B,CANO A.Online ensemble learning with abstaining classifiers for drifting and noisy data streams[J].Applied Soft Computing,2018,68:677-692.
[143]ZHANG H,LIU W,SHAN J,et al.Online Active LearningPaired Ensemble for Concept Drift and Class Imbalance[J].IEEE Access,2018,6:73815-73828.
[144]SIDHU P,SIDHU P,BHATIA M P S,et al.A novel online ensemble approach to handle concept drifting data streams:diversified dynamic weighted majority[J].International Journal of Machine Learning and Cybernetics,2018,9(1):37-61.
[145]OLORUNNIMBE M K,VIKTOR H L,PAQUET E.Dynamic adaptation of online ensembles for drifting data streams[J].Journal of IntelligentInformation Systems,2018,50(2):291-313.
[146]GHOMESHI H,GABER M M,KOVALCHUK Y.A non-canonical hybrid metaheuristic approach to adaptive data stream classification[J].Future Generation Computer Systems,2020,102:127-139.
[147]SHAN J,ZHANG H,LIU W,et al.Online Active Learning Ensemble Framework for Drifted Data Streams[J].IEEE Transactions on Neural Networks and Learning Systems,2019,30(2):486-498.
[148]YANG L,CHEUNG Y M,YUAN Y T.Adaptive Chunk-Based Dynamic Weighted Majority for Imbalanced Data Streams With Concept Drift[J].IEEE Transactions on Neural Networks and Learning Systems,2020,31(8):2764-2778.
[149]CANO A,KRAWCZYK B.Kappa Updated Ensemble for Drifting Data Stream Mining[J].Machine Learning,2020,109(1):175-218.
[150]SCHOLZ M,KLINKENBERG R.An ensemble classifier fordrifting concepts[C]//Proceedings of the Second International Workshop on Knowledge Discovery from Data Streams(IWKDDS’05).2005:53-64.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed