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AMDO: An Over-Sampling Technique for Multi-Class Imbalanced Problems
Yang, Xuebing1,2; Kuang, Qiuming1,2; Zhang, Wensheng1,2; Zhang, Guoping3; Wensheng Zhang
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
2018-09-01
Volume30Issue:9Pages:1672-1685
SubtypeArticle
Abstract ; 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.
KeywordMulti-class Imbalanced Problems Over-sampling Mdo Mixed-type Data
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TKDE.2017.2761347
WOS KeywordDATA-SETS ; NEURAL-NETWORKS ; CLASSIFICATION ; CLASSIFIERS ; SMOTE ; ALGORITHMS ; SYSTEM
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61432008 ; 61472423 ; U1636220)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000440853500004
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20866
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorWensheng Zhang
Affiliation1.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
Recommended Citation
GB/T 7714
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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