Overview of classification methods for complex data streams with concept drift

doi:10.11772/j.issn.1001-9081.2022060881

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1664-1675.DOI: 10.11772/j.issn.1001-9081.2022060881

Special Issue: 综述； CCF第37届中国计算机应用大会 (CCF NCCA 2022)

• The 37 CCF National Conference of Computer Applications (CCF NCCA 2022) • Previous Articles Next Articles

Overview of classification methods for complex data streams with concept drift

Dongliang MU, Meng HAN(), Ang LI, Shujuan LIU, Zhihui GAO

School of Computer Science and Engineering，North Minzu University，Yinchuan Ningxia 750021，China

Received:2022-06-20 Revised:2022-08-09 Accepted:2022-08-12 Online:2022-08-24 Published:2023-06-10
Contact: Meng HAN
About author:MU Dong
liang， born in 1998， M. S. candidate. His research interests include big data mining.
LI Ang， born in 1999， M. S. candidate. His research interests include big data mining.
LIU Shujuan， born in 1998， M. S. candidate. Her research interests include big data mining.
GAO Zhihui， born in 1996， M. S. candidate. Her research interests include big data mining.
Supported by:
National Natural Science Foundation of China(62062004);Natural Science Foundation of Ningxia(2020AAC03216)

概念漂移复杂数据流分类方法综述

穆栋梁, 韩萌(), 李昂, 刘淑娟, 高智慧

北方民族大学计算机科学与工程学院，银川 750021

通讯作者: 韩萌
作者简介:穆栋梁（1998—），男，山西大同人，硕士研究生，CCF会员，主要研究方向：大数据挖掘
韩萌（1982—），女，河南商丘人，教授，博士，CCF会员，主要研究方向：大数据挖掘Email：2003051@nmu.edu.cn
李昂（1999—），男，河南洛阳人，硕士研究生，主要研究方向：大数据挖掘
刘淑娟（1998—），女，河南新乡人，硕士研究生，主要研究方向：大数据挖掘
高智慧（1996—），女，山东临沂人，硕士研究生，主要研究方向：大数据挖掘。
基金资助:
国家自然科学基金资助项目(62062004);宁夏自然科学基金资助项目(2020AAC03216)

Abstract

Abstract:

The traditional classifiers are difficult to cope with the challenges of complex types of data streams with concept drift， and the obtained classification results are often unsatisfactory. Aiming at the methods of dealing with concept drift in different types of data streams， classification methods for complex data streams with concept drift were summarized from four aspects： imbalance， concept evolution， multi-label and noise-containing. Firstly， classification methods of four aspects were introduced and analyzed： block-based and online-based learning approaches for classifying imbalanced concept drift data streams， clustering-based and model-based learning approaches for classifying concept evolution concept drift data streams， problem transformation-based and algorithm adaptation-based learning approaches for classifying multi-label concept drift data streams and noisy concept drift data streams. Then， the experimental results and performance metrics of the mentioned concept drift complex data stream classification methods were compared and analyzed in detail. Finally， the shortcomings of the existing methods and the next research directions were given.

Key words: data stream classification, complex data stream, concept drift, imbalanced data stream, conceptual evolution

摘要：

传统分类器难以应对含概念漂移的复杂类型数据流分类这一难题，且得到的分类效果往往不尽如人意。针对不同类型数据流中处理概念漂移的方法，从不平衡、概念演化、多标签和含噪声4个方面对概念漂移复杂数据流分类方法进行了综述。首先，对基于块的和基于在线的学习方式对不平衡概念漂移数据流、基于聚类和基于模型的学习方式对概念演化概念漂移数据流、基于问题转换和基于算法适应的学习方式对多标签概念漂移数据流和含噪声概念漂移数据流这四个方面的分类方法进行了分析介绍；然后，对所提到概念漂移复杂数据流分类方法的实验结果及性能指标进行了详细的对比和分析；最后，给出了现有方法的不足和下一步研究方向。

关键词: 数据流分类, 复杂数据流, 概念漂移, 不平衡数据流, 概念演化

CLC Number:

TP391.1

Dongliang MU, Meng HAN, Ang LI, Shujuan LIU, Zhihui GAO. Overview of classification methods for complex data streams with concept drift[J]. Journal of Computer Applications, 2023, 43(6): 1664-1675.

穆栋梁, 韩萌, 李昂, 刘淑娟, 高智慧. 概念漂移复杂数据流分类方法综述[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1664-1675.

Figures/Tables 11

References 62

1	丁剑，韩萌，李娟. 概念漂移数据流挖掘算法综述［J］. 计算机科学， 2016， 43（12）： 24-29， 62. 10.11896/j.issn.1002-137X.2016.12.004
	DING J， HAN M， LI J. Review of concept drift data streams mining techniques［J］. Computer Science， 2016， 43（12）： 24-29， 62. 10.11896/j.issn.1002-137X.2016.12.004
2	WU X， LIU H. Application of big data unbalanced classification algorithm in credit risk analysis of insurance companies［J］. Journal of Mathematics， 2022， 2022： No.3899801. 10.1155/2022/3899801
3	TAO X M， LI Q， GUO W J， et al. Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification［J］. Information Sciences， 2019， 487： 31-56. 10.1016/j.ins.2019.02.062
4	NGUYEN T T T， NGUYEN T T， LIEW A W C， et al. An online variational inference and ensemble based multi-label classifier for data streams［C］// Proceedings of the 11th International Conference on Advanced Computational Intelligence. Piscataway： IEEE， 2019： 302-307. 10.1109/icaci.2019.8778594
5	XIA Y L， CHEN K， YANG Y. Multi-label classification with weighted classifier selection and stacked ensemble ［J］. Information Sciences， 2021， 557： 421-442. 10.1016/j.ins.2020.06.017
6	杜诗语，韩萌，申明尧，等. 概念漂移数据流集成分类算法综述［J］. 计算机工程， 2020， 46（1）： 15-24， 30. 10.19678/j.issn.1000-3428.0055747
	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， 30. 10.19678/j.issn.1000-3428.0055747
7	HU H Q， KANTARDZIC M， SETHI T S. No free lunch theorem for concept drift detection in streaming data classification： a review［J］. WIREs： Data Mining and Knowledge Discovery， 2020， 10（2）： No.e1327. 10.1002/widm.1327
8	ZHANG X L， HAN M， WU H X， et al. An overview of complex data stream ensemble classification［J］. Journal of Intelligent and Fuzzy Systems， 2021， 41（2）： 3667-3695. 10.3233/jifs-211100
9	GAO J， FAN W， HAN J W， et al. A general framework for mining concept-drifting data streams with skewed distributions［C］// Proceedings of the 2007 SIAM International Conference on Data Mining. Philadelphia， PA： SIAM， 2007： 3-14. 10.1137/1.9781611972771.1
10	CHEN S， HE H B. SERA： selectively recursive approach towards nonstationary imbalanced stream data mining［C］// Proceedings of the 2009 International Joint Conference on Neural Networks. Piscataway： IEEE， 2009： 522-529. 10.1109/ijcnn.2009.5178874
11	CHEN S， HE H B. Towards incremental learning of nonstationary imbalanced data stream： a multiple selectively recursive approach［J］. Evolving Systems， 2011， 2（1）： 35-50. 10.1007/s12530-010-9021-y
12	HOENS T R， CHAWLA N V. Learning in nonstationary environments with class imbalance［C］// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2012： 168-176. 10.1145/2339530.2339558
13	DITZLER G， POLIKAR R. Incremental learning of concept drift from streaming imbalanced data［J］. IEEE Transactions on Knowledge and Data Engineering， 2013， 25（10）： 2283-2301. 10.1109/tkde.2012.136
14	DITZLER G， POLIKAR R. An ensemble based incremental learning framework for concept drift and class imbalance［C］// Proceedings of the 2010 International Joint Conference on Neural Networks. Piscataway： IEEE， 2010： 1-8. 10.1109/ijcnn.2010.5596764
15	ZHANG Y， DU H L， KE G， et al. Dynamic weighted selective ensemble learning algorithm for imbalanced data streams［J］. The Journal of Supercomputing， 2022， 78（4）： 5394-5419. 10.1007/s11227-021-04084-w
16	MIRZA B， LIN Z P. Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification［J］. Neural Networks， 2016， 80： 79-94. 10.1016/j.neunet.2016.04.008
17	MIRZA B， LIN Z P， LIU N. Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift［J］. Neurocomputing， 2015， 149（Pt A）： 316-329. 10.1016/j.neucom.2014.03.075
18	LI Z， HUANG W C， XIONG Y， et al. Incremental learning imbalanced data streams with concept drift： the dynamic updated ensemble algorithm［J］. Knowledge-Based Systems， 2020， 195： No.105694. 10.1016/j.knosys.2020.105694
19	孙艳歌，王志海，白洋. 一种面向不平衡数据流的集成分类算法［J］. 小型微型计算机系统， 2018， 39（6）： 1178-1183. 10.3969/j.issn.1000-1220.2018.06.011
	SUN Y G， WANG Z H， BAI Y. Ensemble classifier for mining imbalanced data streams［J］. Journal of Chinese Computer Systems， 2018， 39（6）： 1178-1183. 10.3969/j.issn.1000-1220.2018.06.011
20	PRIYA S， UTHRA R A. Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data ［J/OL］. Complex and Intelligent Systems （2021-07-10）［2022-05-23］.. 10.1007/s40747-021-00456-0
21	WANG S， MINKU L L， YAO X. Resampling-based ensemble methods for online class imbalance learning［J］. IEEE Transactions on Knowledge and Data Engineering， 2015， 27（5）： 1356-1368. 10.1109/tkde.2014.2345380
22	SOMASUNDARAM A， REDDY S. Parallel and incremental credit card fraud detection model to handle concept drift and data imbalance［J］. Neural Computing and Applications， 2019， 31（S1）： 3-14. 10.1007/s00521-018-3633-8
23	ANCY S， PAULRAJ D. Handling imbalanced data with concept drift by applying dynamic sampling and ensemble classification model［J］. Computer Communications， 2020， 153： 553-560. 10.1016/j.comcom.2020.01.061
24	梁斌，李光辉，代成龙. 面向概念漂移且不平衡数据流的G-mean加权分类方法［J］. 计算机研究与发展， 2022， 59（12）： 2844-2857. 10.7544/issn1000-1239.20210471
	LIANG B， LI G H， DAI C L. G-mean weighted classification method for imbalanced data stream with concept drift［J］. Journal of Computer Research and Development， 2022， 59（12）： 2844-2857. 10.7544/issn1000-1239.20210471
25	SUN Y G， SUN Y， DAI H H. Two-stage cost-sensitive learning for data streams with concept drift and class imbalance［J］. IEEE Access， 2020， 8： 191942-191955. 10.1109/access.2020.3031603
26	SUN Y G， LI M， LI L， et al. Cost-sensitive classification for evolving data streams with concept drift and class imbalance ［J］. Computational Intelligence and Neuroscience， 2021， 2021： No.8813806. 10.1155/2021/8813806
27	SPINOSA E J， DE CARVALHO A C P L F， GAMA J. OLINDDA： a cluster-based approach for detecting novelty and concept drift in data streams［C］// Proceedings of the 2007 ACM Symposium on Applied Computing. New York： ACM， 2007： 448-452. 10.1145/1244002.1244107
28	DE FARIA E R， DE CARVALHO A C P L F， CARLOS A， et al. MINAS： multiclass learning algorithm for novelty detection in data streams［J］. Data Mining and Knowledge Discovery， 2016， 30（3）： 640-680. 10.1007/s10618-015-0433-y
29	MASUD M M， GAO J， KHAN L， et al. Integrating novel class detection with classification for concept-drifting data streams［C］// Proceedings of the 2009 Joint European Conference on Machine Learning and Knowledge Discovery in Databases， LNCS 5782. Berlin： Springer， 2009： 79-94.
30	MASUD M M， CHEN Q， KHAN L， et al. Classification and adaptive novel class detection of feature-evolving data streams ［J］. IEEE Transactions on Knowledge and Data Engineering， 2013， 25（7）： 1484-1497. 10.1109/tkde.2012.109
31	ZHENG X L， LI P P， HU X G， et al. Semi-supervised classification on data streams with recurring concept drift and concept evolution［J］. Knowledge-Based Systems， 2021， 215： No.106749. 10.1016/j.knosys.2021.106749
32	DIN S U， SHAO J M. Exploiting evolving micro-clusters for data stream classification with emerging class detection［J］. Information Sciences， 2020， 507： 404-420. 10.1016/j.ins.2019.08.050
33	MUSTAFA A M， AYOADE G， AL-NAAMI K， et al. Unsupervised deep embedding for novel class detection over data stream［C］// Proceedings of the 2017 IEEE International Conference on Big Data. Piscataway： IEEE， 2017： 1830-1839. 10.1109/bigdata.2017.8258127
34	HAQUE A， KHAN L， BARON M. SAND： semi-supervised adaptive novel class detection and classification over data stream［C］// Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto， CA： AAAI Press， 2016： 1652-1658. 10.1609/aaai.v30i1.10283
35	HAQUE A， KHAN L， BARON M， et al. Efficient handling of concept drift and concept evolution over stream data［C］// Proceedings of the IEEE 32nd International Conference on Data Engineering. Piscataway： IEEE， 2016： 481-492. 10.1109/icde.2016.7498264
36	LI X J， ZHOU Y， JIN Z Y， et al. A classification and novel class detection algorithm for concept drift data stream based on the cohesiveness and separation index of Mahalanobis distance［J］. Journal of Electrical and Computer Engineering， 2020， 2020： No.4027423. 10.1155/2020/4027423
37	ABDALLAH Z S， GABER M M， SRINIVASAN B， et al. AnyNovel： detection of novel concepts in evolving data streams： an application for activity recognition［J］. Evolving Systems， 2016， 7（2）： 73-93. 10.1007/s12530-016-9147-7
38	MASUD M M， GAO J， KHAN L， et al. Classification and novel class detection in concept-drifting data streams under time constraints［J］. IEEE Transactions on Knowledge and Data Engineering， 2011， 23（6）： 859-874. 10.1109/tkde.2010.61
39	MASUD M M， AL-KHATEEB T M， KHAN L， et al. Detecting recurring and novel classes in concept-drifting data streams ［C］// Proceedings of the IEEE 11th International Conference on Data Mining. Piscataway： IEEE， 2011： 1176-1181. 10.1109/icdm.2011.49
40	FARID D M， RAHMAN C M. Novel class detection in concept-drifting data stream mining employing decision tree［C］// Proceedings of the 7th International Conference on Electrical and Computer Engineering. Piscataway： IEEE， 2012： 630-633. 10.1109/icece.2012.6471629
41	MU X， TING K M， ZHOU Z H. Classification under streaming emerging new classes： a solution using completely-random trees［J］. IEEE Transactions on Knowledge and Data Engineering， 2017， 29（8）： 1605-1618. 10.1109/tkde.2017.2691702
42	GANDHI J， GANDHI V. Novel class detection with concept drift in data stream — AhtNODE［J］. International Journal of Distributed Systems and Technologies， 2020， 11（1）： 15-26. 10.4018/ijdst.2020010102
43	GAO Y， CHANDRA S， LI Y F， et al. SACCOS： a semi-supervised framework for emerging class detection and concept drift adaption over data streams［J］. IEEE Transactions on Knowledge and Data Engineering， 2022， 34（3）： 1416-1426. 10.1109/tkde.2020.2993193
44	BOUGUELIA M R， NOWACZYK S， PAYBERAH A H. An adaptive algorithm for anomaly and novelty detection in evolving data streams［J］. Data Mining and Knowledge Discovery， 2018， 32（6）： 1597-1633. 10.1007/s10618-018-0571-0
45	QU W， ZHANG Y， ZHU J P， et al. Mining multi-label concept-drifting data streams using dynamic classifier ensemble［C］// Proceedings of the 2009 Asian Conference on Machine Learning， LNCS 5828. Berlin： Springer， 2009： 308-321.
46	JÚNIOR J D C， FARIA E R， SILVA J A， et al. Novelty detection for multi-label stream classification［C］// Proceedings of the 8th Brazilian Conference on Intelligent Systems. Piscataway： IEEE， 2019： 144-149. 10.1109/bracis.2019.00034
47	JÚNIOR J D C， FARIA E R， SILVA J A， et al. Pruned sets for multi-label stream classification without true labels［C］// Proceedings of the 2019 International Joint Conference on Neural Networks. Piscataway： IEEE， 2019： 1-8. 10.1109/ijcnn.2019.8851788
48	SPYROMITROS-XIOUFIS E， SPILIOPOULOU M， TSOUMAKAS G， et al. Dealing with concept drift and class imbalance in multi-label stream classification［C］// Proceedings of the 22nd International Joint Conference on Artificial Intelligence. California： ijcai.org， 2011： 1583-1588.
49	WANG P， ZHANG P， GUO L. Mining multi-label data streams using ensemble-based active learning［C］// Proceedings of the 2012 SIAM International Conference on Data Mining. Philadelphia， PA： SIAM， 2012： 1131-1140. 10.1137/1.9781611972825.97
50	NGUYEN T T， NGUYEN T T T， LUONG A V， et al. Multi-label classification via label correlation and first order feature dependance in a data stream［J］. Pattern Recognition， 2019， 90： 35-51. 10.1016/j.patcog.2019.01.007
51	SUN Y G， SHAO H， WANG S S. Efficient ensemble classification for multi-label data streams with concept drift［J］. Information， 2019， 10（5）： No.158. 10.3390/info10050158
52	ZHANG M L， ZHOU Z H. ML-KNN： a lazy learning approach to multi-label learning［J］. Pattern Recognition， 2007， 40（7）： 2038-2048. 10.1016/j.patcog.2006.12.019
53	READ J， BIFET A， HOLMES G， et al. Scalable and efficient multi-label classification for evolving data streams［J］. Machine Learning， 2012， 88（1/2）： 243-272. 10.1007/s10994-012-5279-6
54	ROSEBERRY M， CANO A. Multi-label kNN classifier with self adjusting memory for drifting data streams ［C］// Proceedings of the 2nd International Workshop on Learning with Imbalanced Domains： Theory and Applications. New York： JMLR.org， 2018： 23-37. 10.1145/3363573
55	ROSEBERRY M， KRAWCZYK B， CANO A. Multi-label punitive knn with self-adjusting memory for drifting data streams［J］. ACM Transactions on Knowledge Discovery from Data， 2019， 13（6）： No.60. 10.1145/3363573
56	ALBERGHINI G， BARBON JUNIOR S， CANO A. Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams［J］. Neurocomputing， 2022， 481： 228-248. 10.1016/j.neucom.2022.01.075
57	袁泉，郭江帆. 新型含噪数据流集成分类的算法［J］. 计算机应用， 2018， 38（6）： 1591-1595. 10.11772/j.issn.1001-9081.2017122900
	YUAN Q， GUO J F. New ensemble classification algorithm for data stream with noise［J］. Journal of Computer Applications， 2018， 38（6）： 1591-1595. 10.11772/j.issn.1001-9081.2017122900
58	MYINT T M， LYNN K T. Handling the concept drifts based on ensemble learning with adaptive windows［J］. IAENG International Journal of Computer Science， 2021， 48（3）： No.3.
59	LUO S L， ZHAO W X， PAN L M. Online GBDT with chunk dynamic weighted majority learners for noisy and drifting data streams ［J］. Neural Processing Letters， 2021， 53（5）： 3783-3799. 10.1007/s11063-021-10565-z
60	CHEN D Z， YANG Q L， LIU J M， et al. Selective prototype-based learning on concept-drifting data streams［J］. Information Sciences， 2020， 516： 20-32. 10.1016/j.ins.2019.12.046
61	LI P P， WU X D， HU X G， et al. Learning concept-drifting data streams with random ensemble decision trees［J］. Neurocomputing， 2015， 166： 68-83. 10.1016/j.neucom.2015.04.024
62	KRAWCZYK B， CANO A. Online ensemble learning with abstaining classifiers for drifting and noisy data streams［J］. Applied Soft Computing， 2018， 68： 677-692. 10.1016/j.asoc.2017.12.008

算法	HyperPlane	Sea	Electricity
SERA		√
Learn++.CDS	√	√	√
Learn++.NIE	√	√	√
ECISD		√
DWSE	√	√
MOS-ELM		√	√
DUE	√	√	√
TSCS	√	√	√
CSDS	√	√	√
OGUEIL	√	√	√

算法	HyperPlane	Sea	Electricity
SERA		√
Learn++.CDS	√	√	√
Learn++.NIE	√	√	√
ECISD		√
DWSE	√	√
MOS-ELM		√	√
DUE	√	√	√
TSCS	√	√	√
CSDS	√	√	√
OGUEIL	√	√	√

算法	KddCup	Forest Cover	Pamap
MINAS	√
MineClass	√	√
MCM		√
ESCR		√
EMC	√		√
DAE		√	√
SAND		√	√
ECHO			√
C&NCBM	√
ECSMiner	√	√
SCANR	√
NCDC	√
SENCForest	√
AhtNODE	√
SACCOS	√		√

算法	KddCup	Forest Cover	Pamap
MINAS	√
MineClass	√	√
MCM		√
ESCR		√
EMC	√		√
DAE		√	√
SAND		√	√
ECHO			√
C&NCBM	√
ECSMiner	√	√
SCANR	√
NCDC	√
SENCForest	√
AhtNODE	√
SACCOS	√		√

数据流类型	算法	方法	数据集	优点	缺点
概念演化概念漂移数据流	OLINDDA	k-means	Breast、 Biomed、 Iris等	单一学习策略解决了数据流上的概念漂移和新颖类别探测问题	无法解决数据流上多个已知类别的情况
	MLNAS	决策模型多分类	KddCup、 Forest Cover等	将新颖性检测视为一个多类任务	对噪声和异常值的干扰处理还有待提升
	MineClass	KNN 决策树	KddCup、 Forest Cover等	非参数方法，不假设任何特定的数据分布	对比算法太少、使用的数据集太少
	MCM	自适应方法基尼系数	Twitter、 Forest Cover等	滤掉了大部分的异常值，运行时间短	类在以前未使用的特征空间中被发现时才会将其检测为新类
	ESCR	半监督 J-S散度	HyperPlane、 Forest Cover、 Wave等	提出了动态规划、递归函数等构建策略，有选择地执行变化检测模块，提高了算法的效率	在时间效率上有待优化
	EMC	在线微簇	KddCup、 Forest Cover等	允许从噪声实例中区分概念漂移和概念演化	需要大量的微蔟才能更有效捕捉漂移，从而增加了时空复杂度
	DAE	无监督多维变化点检测	Packets、 Forest Cover等	增加噪声量，DAE的性能会提高，新的类检测速度快	在算法中假设金额标签都是周期性的
	SAND	半监督置信度分数	Forest Cover、 Pamap等	概念漂移和概念演化的寻址高效	检测器假设每次只出现一个新类，时间开销大
	ECHO	不确定性采样半监督	Forest Cover、 HyperPlane、 Pamap	通过检测分类器置信估计的变化，促进概念漂移和概念演化的寻址	检测器假设每次只出现一个新类
	C&NCBM	马氏距离基于块	KddCup、 ArtificialCDS	对概念漂移数据流中出现的新类具有更强的分类鲁棒性。	由于受添加马氏距离的影响，具有较长的运行时间
	AnyNovel	主动学习基于类	Wisdm、 Opportunity	能够有效识别小规模的类，很好地区分概念漂移和新概念	为每个数据集设置的参数需要手动调整
	ECSMiner	延迟数据标记分类决策	KddCup、 Forest Cover等	正确区分了概念漂移和概念演化，避免了在概念漂移的情况下对新类的错误检测	空间开销大
	SCANR	辅助集合	SynC10、 KddCup	降低了错误率和误报率，能够检测出重复出现的新类	辅助集成有待优化，额外运行时间长
	SENForest	随机森林	KddCup、 MNIST等	模型能够正确识别足够多的新类样本	对于应用在大规模级别的数据挖掘问题有一定的局限性
	AhtNODE	Hoeffding树滑动窗口	Hyperplane、 KddCup等	其分类性能优于集成分类器，可以直接应用于流数据	不适合处理混合属性的新类检测情况
	SACCOS	基于相互图模型半监督	KddCup、 Pamap等	克服了少数数据实例具有较大特征值的影响，新颖类的实例标签在检测后就可以使用	对噪声敏感，未解决新颖类和现有类可能存在重叠的情况
多标签概念漂移数据流	DCEBR	加权投票二元相关	Syn、 RCV1	对概念漂移具有更好的跟踪效果	每一个标签建立一个二值分类器，会占用大量的内存空间
	EALF	主动学习最大后验加权	RCV1-V2、 Imdb等	避免了过拟合问题，降低了标签成本	独立地更新每个类的集合，导致时间开销较大
	MW	批量增量阈值多窗口	Tmc2007、 Imdb等	解决了类不平衡和多重概念漂移	受到阈值限制的影响，需要采取合适的阈值策略
	MLAW	J-S散度周期性加权	Imdb、 Tmc2007等	能处理不同类型的概念漂移	时空消耗有待优化
	MINAS-BR	二元关联无监督	MOA-3C-5C-2D等	能同时处理多标签数据流中的概念漂移和概念演化问题	未应用在真实多标签数据集上
	MINAS-PS	无监督 PS	MOA-3C-2D、 MOA-5C-2D	能适应不同类型的概念漂移	与当前主流算法的对比实验少
	BBML	Hoeffding不等式	20NG、 Slashdot等	可以确定预测标签数量，能有效处理数据流中的缺失值和概念漂移	时间复杂度高
	MLSAMkNN	KNN	20NG、 Flags等	适用于处理经历各种和混合概念漂移的多标签数据流	性能以运行时间为代价
	MLSAMPkNN	惩罚机制自适应窗口	Imdb、 Scene等	不需要繁琐模型选择和参数调优	在稀疏的标签流中学习性能较差
	AESAKNNS	KNN 自适应	Birds、 Virus等	解决了其他各种多标签数据问题	没有与其他算法进行时空效率的对比实验

Overview of classification methods for complex data streams with concept drift

概念漂移复杂数据流分类方法综述

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 62

Related Articles 10

Recommended Articles

Metrics

算法	年份	方法	数据集	对比算法	优缺点
ECANCD	2018	噪声过滤机制假设检验方法	HyperPlane、 KddCup等	OzaBag、 Ozaboost等	能检测概念漂移和过滤噪声，拥有较高分类精度；在不完全标记的数据流环境中实现漂移检测，分类有局限性
A-AUE2	2021	BSS 自适应窗口	Sea、 HyperPlane等	AUE2、 ADWIN等	对块大小的依赖性较小；在时空性能上有所不足
GBDT	2021	在线框架损失函数	Sea、 Weather等	OABM、 EACD等	能在有噪声的数据流中准确地识别出真实模式；实例数少时分类精度不高
SPL	2020	基于实例动态选择实例	Spam、 Sensor等	AUE2、 OzaBag等	具有较低空间复杂度，能有效区分噪声、离群值和漂移实例；使用自适应窗口关注最近的数据，比传统的单模型算法慢

[1]	Hua HUANG, Ziyi YANG, Xiaolong LI, Chuang LI. Predictive business process monitoring method based on concept drift [J]. Journal of Computer Applications, 2024, 44(10): 3167-3176.
[2]	Zhiqiang CHEN, Meng HAN, Hongxin WU, Muhang LI, Xilong ZHANG. Multi-stage weighted concept drift detection method [J]. Journal of Computer Applications, 2023, 43(3): 776-784.
[3]	YIN Chunyong, ZHANG Guojie. Ensemble classification model for distributed drifted data streams [J]. Journal of Computer Applications, 2021, 41(7): 1947-1955.
[4]	BAI Dongying, YI Yaxing, WANG Qingchao, YU Zhiyong. Gradual multi-kernel learning method for concept drift [J]. Journal of Computer Applications, 2019, 39(9): 2494-2498.
[5]	ZHANG Yitian, YU Jiong, LU Liang, LI Ziyang. Task scheduling strategy based on data stream classification in Heron [J]. Journal of Computer Applications, 2019, 39(4): 1106-1116.
[6]	YUAN Quan, GUO Jiangfan. New ensemble classification algorithm for data stream with noise [J]. Journal of Computer Applications, 2018, 38(6): 1591-1595.
[7]	LIU Mao ZHANG Dongbo ZHAO Yuanyuan. Concept drift detection based on distance measurement of overlapped data windows [J]. Journal of Computer Applications, 2014, 34(2): 542-545.
[8]	LI Nan GUO Gong-de CHEN Li-fei. Concept drift detection method with limited amount of labeled data [J]. Journal of Computer Applications, 2012, 32(08): 2176-2185.
[9]	LI Nan GUO Gong-de. Ensemble classification algorithm for high speed data stream [J]. Journal of Computer Applications, 2012, 32(03): 629-633.
[10]	. Incremental feature selection algorithm for data stream classification [J]. Journal of Computer Applications, 2010, 30(9): 2321-2323.