Improved over-sampling techniques based on sparse representation for imbalance problem-Reference-Cited by-同舟云学术

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Improved over-sampling techniques based on sparse representation for imbalance problem

Published:2018-09-26 Issue:5 Volume:22 Page:939-958
ISSN:1088-467X
Container-title:Intelligent Data Analysis
language:
Short-container-title:IDA

Author:

Zou Xionggao¹²,Feng Yueping¹,Li Huiying¹²,Jiang Shuyu³

Affiliation:

1. College of Computer Science and Technology, Jilin University, Changchun, Jilin, China

2. Symbol Computation and Knowledge Engineer of Ministry of Education, Jilin University, Changchun, Jilin, China

3. College of Software, Jilin University, Changchun, Jilin, China

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science

Reference31 articles.

1. E. Chen, Y. Lin and H. Xiong, Exploiting probabilistic topic models to improve text categorization under class imbalance, Information Processing & Management 47 (2011), 202–214.

2. Learning from imbalanced data;He;IEEE Transactions on Knowledge and Data Engineering,2009

3. A survey of predictive modeling on imbalanced domains;Branco;ACM Computing Surveys (CSUR),2016

4. SMOTE: synthetic minority over-sampling technique;Chawla;Journal of Artificial Intelligence Research,2002

5. Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem;Bunkhumpornpat;Advances in Knowledge Discovery and Data Mining,2009

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimization of SMOTE for imbalanced data based on AdaRBFNN and hybrid metaheuristics;Intelligent Data Analysis;2021-04-20

2. A novel adaptive k-NN classifier for handling imbalance: Application to brain MRI;Intelligent Data Analysis;2020-07-15

3. Sequential prediction for imbalanced data stream via weighted OS-ELM and dynamic GAN;Intelligent Data Analysis;2019-11-08

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