AMDO: An Over-Sampling Technique for Multi-Class Imbalanced Problems
Yang, Xuebing1,2; Kuang, Qiuming1,2; Zhang, Wensheng1,2; Zhang, Guoping3; Wensheng Zhang
2018-09-01
发表期刊IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷号30期号:9页码:1672-1685
文章类型Article
摘要 ; Multi-class imbalanced problems have attracted growing attention from the real-world classification tasks in engineering. The underlying skewed distribution of multiple classes poses difficulties for learning algorithms, which becomes more challenging when considering overlapping between classes, lack of representative data, and mixed-type data. In this work, we address this problem in a data-oriented way. Motivated by a recently proposed over-sampling technique designed for numeric data sets, Mahalanobis Distance-based Over-sampling (MDO), we use this technique to capture the covariance structure of the minority class and to generate synthetic samples along the probability contours for learning algorithms. Based on MDO, we further improve the over-sampling strategy and generalize it for mixed-type data sets. The established technique, Adaptive Mahalanobis Distance-based Over-sampling (AMDO), introduces GSVD (Generalized Singular Value Decomposition) for mixed-type data, develops a partially balanced resampling scheme and optimizes the sample synthesis. Theoretical analysis is conducted to demonstrate the reasonability of AMDO. Extensive experimental testing is performed on 15 multi-class imbalanced benchmarks and two data sets for precipitation phase recognition in comparison with several state-of-the-art multi-class imbalanced learning methods. The results validate the effectiveness and robustness of our proposal.
关键词Multi-class Imbalanced Problems Over-sampling Mdo Mixed-type Data
WOS标题词Science & Technology ; Technology
DOI10.1109/TKDE.2017.2761347
关键词[WOS]DATA-SETS ; NEURAL-NETWORKS ; CLASSIFICATION ; CLASSIFIERS ; SMOTE ; ALGORITHMS ; SYSTEM
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61432008 ; 61472423 ; U1636220)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号WOS:000440853500004
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20866
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Wensheng Zhang
作者单位1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.CMA, Joint Lab Meteorol Data & Machine Learning, Publ Meteorol Serv Ctr, Beijing 100081, Peoples R China
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Yang, Xuebing,Kuang, Qiuming,Zhang, Wensheng,et al. AMDO: An Over-Sampling Technique for Multi-Class Imbalanced Problems[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2018,30(9):1672-1685.
APA Yang, Xuebing,Kuang, Qiuming,Zhang, Wensheng,Zhang, Guoping,&Wensheng Zhang.(2018).AMDO: An Over-Sampling Technique for Multi-Class Imbalanced Problems.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,30(9),1672-1685.
MLA Yang, Xuebing,et al."AMDO: An Over-Sampling Technique for Multi-Class Imbalanced Problems".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 30.9(2018):1672-1685.
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