A New Oversampling Method Based on Triangulation of Sample Space-Reference-Cited by-同舟云学术

A New Oversampling Method Based on Triangulation of Sample Space

Published:2024-02 Issue:2 Volume:54 Page:774-786
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Chen Yueqi¹^ORCID,Pedrycz Witold²^ORCID,Wang Jian³^ORCID,Zhang Chao¹^ORCID,Yang Jie¹^ORCID

Affiliation:

1. School of Mathematical Sciences and the Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian University of Technology, Dalian, China

2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

3. College of Science, China University of Petroleum (East China), Qingdao, China

Funder

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221021/10403398/10284916.pdf?arnumber=10284916

Reference55 articles.

1. Learning a Distance Metric by Balancing KL-Divergence for Imbalanced Datasets

2. Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer

3. Competition-Driven Multimodal Multiobjective Optimization and Its Application to Feature Selection for Credit Card Fraud Detection

4. Crowd Decision Making: Sparse Representation Guided by Sentiment Analysis for Leveraging the Wisdom of the Crowd

5. AUC-Based Extreme Learning Machines for Supervised and Semi-Supervised Imbalanced Classification

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