面向概念漂移和类不平衡数据流的在线分类算法
Online Classification Algorithm for Concept Drift and Class Imbalance Data Stream
- 电子学报 2022年50卷第3期 页码：585-597
- 作者机构：
深圳大学计算机与软件学院, 广东深圳 518060
- 作者简介：
[ "陆克中 男,1982年生.博士,教授.主要研究方向为大数据计算、并行计算和数据挖掘." ]
[ "陈超凡 男,1998年生.硕士研究生在读.主要研究方向为机器学习，深度学习以及数据分析." ]
[ "蔡 桓 男,1993年生.硕士研究生.主要研究方向为数据挖掘和机器学习." ]
[ "吴定明（通讯作者） 女,1982年生.博士, 副教授.主要研究方向为大数据计算和数据挖掘.E-mail1:dingming@szu.edu.cn" ]
- 基金信息：国家自然科学基金项目(61502310);广东省自然科学基金(2019A1515011721;2019A1515011064);深圳市基础研究资助项目(20200806102941001)
DOI：10.12263/DZXB.20210094
中图分类号： TP391收稿：2021-01-12，
修回：2021-12-14，
纸质出版：2022-03-25
- 稿件说明：
移动端阅览
陆克中,陈超凡,蔡桓等.面向概念漂移和类不平衡数据流的在线分类算法[J].电子学报,2022,50(03):585-597.
LU Ke-zhong,CHEN Chao-fan,CAI Huan,et al.Online Classification Algorithm for Concept Drift and Class Imbalance Data Stream[J].ACTA ELECTRONICA SINICA,2022,50(03):585-597.
陆克中,陈超凡,蔡桓等.面向概念漂移和类不平衡数据流的在线分类算法[J].电子学报,2022,50(03):585-597. DOI： 10.12263/DZXB.20210094.
LU Ke-zhong,CHEN Chao-fan,CAI Huan,et al.Online Classification Algorithm for Concept Drift and Class Imbalance Data Stream[J].ACTA ELECTRONICA SINICA,2022,50(03):585-597. DOI： 10.12263/DZXB.20210094.
摘要
数据流是大数据的重要形式，数据流分类是数据挖掘的重要任务之一，该任务在现实生活中有着巨大的应用前景，因此得到了研究者们的广泛关注.概念漂移和类不平衡是影响数据流分类性能的两个核心问题，但目前大多数算法都只考虑处理两者之一，并且大多数算法过于理想，只能在人工设置的数据流上才能发挥较好的性能，无法适用于复杂的真实数据流.针对这一问题，提出了一种同时处理概念漂移和类不平衡复杂数据流的算法——具有自适应遗忘因子的加权在线顺序极限学习机集成算法.该算法首先融合加权机制和遗忘机制，初步提出具有遗忘机制的加权在线顺序极限学习机算法.为了更好地适应复杂数据流，进一步以初步算法为基分类器，设计包含自适应遗忘因子和概念漂移检测机制的在线集成策略.大量仿真实验表明，所提算法在所有数据集上都取得了最佳的Gmean值，具有更好的概念漂移和类不平衡适应能力，表现出了更稳定、更平衡以及更准确的分类效果.
Abstract
Data stream is an important form of big data
and data stream classification is one of the most important tasks in data mining. This task finds wide application in our life
so it has been attracting great attention of researchers. Concept drift and class imbalance are two main issues that affect the performance of data stream classification algorithms. However
most solutions only address one of these two issues. Even worse
most algorithms can only achieve good performance on data streams under manual settings and cannot be applied to real complex data streams. To solve this problem
an ensemble algorithm of weighted online sequential extreme learning machine with adaptive forgetting factor is proposed to deal with both conceptual drift and imbalance on complex data streams. The proposed algorithm is a weighted online sequential limit learning machine that integrates a weighting mechanism and a forgetting mechanism. In order to adapt to complex data streams
an online integration strategy including adaptive forgetting factor and concept drift detection mechanism was designed as a classifier. Extensive simulation experiments show that the proposed algorithm achieves the best Gmean value on all data sets
has the ability to deal with concept drift and class imbalance
and presents stable
balanced and accurate classification effects.
关键词
Keywords
references
PRIYA S , UTHRA RA . Comprehensive analysis for class imbalance data with concept drift using ensemble based classification [J]. J Ambient Intell Human Comput ， 2020 .
WANKHADE K K , DONGRE S S , JONDHALE K C . Data stream classification: A Review [J]. Iran J Comput ， 2020 ， 3 （ 2 ）： 239 ‑ 260 .
SCHLIMMER JEFFREY C , GRANGER RICHARD H . Incremental learning from noisy data [J]. 1986 ， 1 ( 3 )： 317 ‑ 354 .
KHANDEKAR V S , SRINATH P . Non-stationary data stream analysis: State-of-the-Art challenges and solutions [C]// Proceeding of International Conference on Computational Science and Applications ， Algorithms for Intelligent Systems . Singapore ： Springer ， 2020 ： 67 ‑ 80 .
MAO W , WANG J , HE L , et al . Online sequential prediction of imbalance data with two-stage hybrid strategy by extreme learning machine [J]. Neurocomputing ， 2017 ， 261 ： 94 ‑ 105 .
NITIN M , ANANT V N . A survey on effects of class imbalance in data pre-processing stage of classification problem [J]. International Journal of Computational Systems Engineering ， 2020 ， 6 ( 2 )： 63 - 75 .
ZHANG B , CHEN Y . Research on detection and integration classification based on concept drift of data stream [J]. EURASIP Journal on Wireless Communications and Networking ， 2019 ， 86 . DOI: 10. 1186/s13638-019-1408-2 http://dx.doi.org/10.1186/s13638-019-1408-2 .
LIANG N , HUANG G , SARACHANDREN P , SUNDARARAJAN N . A fast and sccurate online sequential learning algorithm for feedforward networks [J]. IEEE Trans Neur Netw， 2006 ， 17 ( 6 )： 1411 ‑ 1423 .
HUANG G , ZHU Q , SIEW C . Extreme learning machine : Theory and applications [J]. Neurocomputing ， 2006 ， 70 ( 1-3 )： 489 ‑ 501 .
MIRZA B , LIN Z , TOH KA . Weighted online sequential extreme learning machine for class imbalance learning [J]. Neural Process Lett ， 2013 ， 38 ： 465 ‑ 486 .
MIRZA B , LIN Z , CAO J . Voting based weighted online sequential extreme learning machine for imbalance multi-Class classification [C]// 2015 IEEE International Symposium on Circuits and Systems(ISCAS) ， Lisbon ： IEEE ， 2015 ： 565 ‑ 568 .
KLIKOWSKI J , WONIAK M . Multi sampling random subspace ensemble for imbalanced data stream classification [C]// Progress of Computer Recognition Systems ， CORES 2019 . Berlin ： Springer ， 2020 ： 360 ‑ 369 .
ZHU H , LIN G , ZHOU M , et al . Optimizing weighted extreme learning machines for imbalanced classification and application to credit card fraud detection [J]. Neurocomputing ， 2020 ， 407 ： 50 ‑ 62 .
HU W , ZHANG B . Study of sampling techniques and algorithms in data stream environments [C]// 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery . Chongqing, China ： IEEE ， 2012 ： 1028 ‑ 1034 .
NGUYEN H M , COOPER E W , KAMEI K . Online learning from imbalanced data streams [C]// 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR) . Dalian, China ： IEEE ， 2011 ： 347 ‑ 352 .
DU Z , LI X , ZHENG Z , ZHANG G , MAO Q . Extreme learning machine based on regularization and forgetting factor and its application in fault prediction [J]. Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument ， 2015 ， 36 ( 7 )： 1546 ‑ 1553 .
YANG R , XU S , FENG L . An ensemble extreme learning machine for data stream classification [J]. Algorithms ， 2018 ， 11 ( 7 ). DOI: 10. 3390/a11070107 http://dx.doi.org/10.3390/a11070107 .
SIMON H . Adaptive Filter Theory [M]. Upper Saddle River ： Prentice Hall ， 2002 .
ZHAI T , GAO Y , WANG H , CAO L . Classification of high-dimensional evolving data streams via a resource-efficient online ensemble [J]. Data Mining and Knowledge Discovery ， 2017 ， 31 ( 5 )： 1242 ‑ 1265
MINKU L L , WHITE A P , YAO X . The impact of diversity on online ensemble learning in the presence of concept Drift [J]. IEEE Transactions on Neural Networks ， 2011 ， 22 ( 10 )： 1517 ‑ 1531 .
GOMES H M , READ J , BIFET A . Streaming random patches for evolving data stream classification [C]// IEEE International Conference on Data Mining (ICDM) ， Beijing, China ： IEEE ， 2019 ： 240 ‑ 249 .
KUBAT M , HOLTE R , MATWIN S . Learning when Negative Examples Abound [M]. Berlin ： Springer ， 1997 .
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