Equalization ensemble for large scale highly imbalanced data classification

doi:10.1016/j.knosys.2022.108295

	Equalization ensemble for large scale highly imbalanced data classification
	Ren, Jinjun1,2 ; Wang, Yuping 1; Mao, Mingqian 1; Cheung, Yiu-ming 3
	2022-04-22
发表期刊	Knowledge-Based Systems
卷号	242
摘要	The class-imbalance problem has been widely distributed in various research fields. The larger the data scale and the higher the data imbalance, the more difficult the proper classification. For large-scale highly imbalanced data sets, the ensemble method based on under-sampling is one of the most competitive techniques among the existing techniques. However, it is susceptible to improperly sampling strategies, easy to lose the useful information of the majority class, and not easy to generalize the learning model. To overcome these limitations, we propose an equalization ensemble method (EASE) with two new schemes. First, we propose an equalization under-sampling scheme to generate a balanced data set for each base classifier, which can reduce the impact of class imbalance on the base classifiers; Second, we design a weighted integration scheme, where the G-mean scores obtained by base classifiers on the original imbalanced data set are used as the weights. These weights can not only make the better-performed base-classifiers dominate the final classification decision, but also adapt to a variety of imbalanced data sets with different scales while avoiding the occurrence of some extremely bad situations. Experimental results on three metrics show that EASE increases the diversity of base classifiers and outperforms twelve state-of-the-art methods on the imbalanced data sets with different scales. © 2022 Elsevier B.V.
关键词	Equalizers Base classifiers Data classification Ensemble learning Equalisation Imbalanced data Imbalanced data classification Imbalanced dataset Large scale data Large-scales Under-sampling
DOI	10.1016/j.knosys.2022.108295
收录类别	EI ; SCIE
ISSN	0950-7051
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000788138900003
出版者	Elsevier B.V.
EI入藏号	20220811684995
原始文献类型	Journal article (JA)
EISSN	1872-7409
引用统计	被引频次：21[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/33096
专题	信息工程与人工智能学院
作者单位	1.School of Computer Science and Technology, Xidian University, Xi'an, ShaanXi; 710071, China; 2.School of Infomation Engineering, LanZhou University of Finance and Economics, LanZhou, GanSu; 730101, China; 3.Department of Computer Science, Hong Kong Baptist University, Hong Kong
第一作者单位	兰州财经大学
推荐引用方式 GB/T 7714	Ren, Jinjun,Wang, Yuping,Mao, Mingqian,et al. Equalization ensemble for large scale highly imbalanced data classification[J]. Knowledge-Based Systems,2022,242.
APA	Ren, Jinjun,Wang, Yuping,Mao, Mingqian,&Cheung, Yiu-ming.(2022).Equalization ensemble for large scale highly imbalanced data classification.Knowledge-Based Systems,242.
MLA	Ren, Jinjun,et al."Equalization ensemble for large scale highly imbalanced data classification".Knowledge-Based Systems 242(2022).