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

• 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

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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

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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

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