Multiset Feature Learning for Highly Imbalanced Data Classification

doi:10.1109/TPAMI.2019.2929166

	Multiset Feature Learning for Highly Imbalanced Data Classification
	Jing, Xiao-Yuan 1,2,3; Zhang, Xinyu 1; Zhu, Xiaoke 4; Wu, Fei 3; You, Xinge 5; Gao, Yang 6; Shan, Shiguang 7; Yang, Jing-Yu 8
	2021
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	43 期号:1 页码:139-156
摘要	With the expansion of data, increasing imbalanced data has emerged. When the imbalance ratio (IR) of data is high, most existing imbalanced learning methods decline seriously in classification performance. In this paper, we systematically investigate the highly imbalanced data classification problem, and propose an uncorrelated cost-sensitive multiset learning (UCML) approach for it. Specifically, UCML first constructs multiple balanced subsets through random partition, and then employs the multiset feature learning (MFL) to learn discriminant features from the constructed multiset. To enhance the usability of each subset and deal with the nonlinearity issue existed in each subset, we further propose a deep metric based UCML (DM-UCML) approach. DM-UCML introduces the generative adversarial network technique into the multiset constructing process, such that each subset can own similar distribution with the original dataset. To cope with the non-linearity issue, DM-UCML integrates deep metric learning with MFL, such that more favorable performance can be achieved. In addition, DM-UCML designs a new discriminant term to enhance the discriminability of learned metrics. Experiments on eight traditional highly class-imbalanced datasets and two large-scale datasets indicate that: the proposed approaches outperform state-of-the-art highly imbalanced learning methods and are more robust to high IR.
关键词	Highly imbalanced data classification multiset feature learning deep metric learning generative adversarial network cost-sensitive factor weighted uncorrelated constraint
DOI	10.1109/TPAMI.2019.2929166
收录类别	SCI
语种	英语
资助项目	NSFC-Key Project of General Technology Fundamental Research United Fund[U1736211] ; National Natural Science Foundation of China[61672208] ; National Natural Science Foundation of China[61702280] ; National Natural Science Foundation of China[61772220] ; National Natural Science Foundation of China[61432008] ; key research and development program of China[2016YFE0121200] ; Key Science and Technology Innovation Program of Hubei Province[2017AAA017] ; Key Science and Technology Innovation Program of Hubei Province[2018ACA135] ; Natural Science Foundation Key Project for Innovation Group of Hubei Province[2018CFA024] ; Natural Science Foundation of Jiangsu Province[BK20170900] ; National Postdoctoral Program for Innovative Talents[BX20180146] ; Higher Education Institution Key Research Projects of Henan Province[19A520001]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000597206900010
出版者	IEEE COMPUTER SOC
引用统计	被引频次：79[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16518
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jing, Xiao-Yuan; Zhu, Xiaoke; Wu, Fei
作者单位	1.Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China 2.Guangdong Univ Petrochem Technol, Sch Comp, Maoming 525000, Peoples R China 3.Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210003, Peoples R China 4.Henan Univ, Henan Key Lab Big Data Anal & Proc, Kaifeng 475004, Peoples R China 5.Huazhong Univ Sci & Technol, Dept Elect & Informat Engn, Wuhan 430074, Peoples R China 6.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210094, Peoples R China 7.Chinese Acad Sci, Inst Comp Technol, CAS, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 8.Nanjing Univ Sci & Technol, Coll Comp Sci, Nanjing 210094, Peoples R China
推荐引用方式 GB/T 7714	Jing, Xiao-Yuan,Zhang, Xinyu,Zhu, Xiaoke,et al. Multiset Feature Learning for Highly Imbalanced Data Classification[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2021,43(1):139-156.
APA	Jing, Xiao-Yuan.,Zhang, Xinyu.,Zhu, Xiaoke.,Wu, Fei.,You, Xinge.,...&Yang, Jing-Yu.(2021).Multiset Feature Learning for Highly Imbalanced Data Classification.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,43(1),139-156.
MLA	Jing, Xiao-Yuan,et al."Multiset Feature Learning for Highly Imbalanced Data Classification".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 43.1(2021):139-156.