Stop Oversampling for Class Imbalance Learning: A Review-Reference-Cited by-同舟云学术

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Stop Oversampling for Class Imbalance Learning: A Review

Published:2022 Issue: Volume:10 Page:47643-47660
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Tarawneh Ahmad S.¹^ORCID,Hassanat Ahmad B.²^ORCID,Altarawneh Ghada Awad³,Almuhaimeed Abdullah⁴^ORCID

Affiliation:

1. Department of Algorithms and Their Applications, Eötvös Loránd University, Budapest, Hungary

2. Faculty of Information Technology, Mutah University, Karak, Jordan

3. Department of Accounting, Mutah University, Karak, Jordan

4. The National Centre for Genomics and Bioinformatics, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09761871.pdf?arnumber=9761871

Reference189 articles.

1. Fuzzy-rough imbalanced learning for the diagnosis of High Voltage Circuit Breaker maintenance: The SMOTE-FRST-2T algorithm

2. Noise Reduction A Priori Synthetic Over-Sampling for class imbalanced data sets

3. Automatic Determination of Neighborhood Size in SMOTE

4. Stochastic Sensitivity Oversampling Technique for Imbalanced Data

5. Adaptive multi-objective swarm fusion for imbalanced data classification

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