Majority re-sampling via sub-class clustering for imbalanced datasets-Reference-Cited by-同舟云学术

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Majority re-sampling via sub-class clustering for imbalanced datasets

Published:2023-01-10 Issue: Volume: Page:1-16
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Ke Shih-Wen¹,Tsai Chih-Fong¹,Pan Yi-Ying¹,Lin Wei-Chao²³

Affiliation:

1. Department of Information Management, National Central University, Taoyuan, Taiwan

2. Department of Information Management, Chang Gung University, Taoyuan, Taiwan

3. Department of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan

Funder

the Chang Gung Memorial Hospital at Linkou

Ministry of Science and Technology, Taiwan

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Theoretical Computer Science,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2023.2165715

Reference68 articles.

1. An improved dynamic sampling back-propagation algorithm based on mean square error to face the multi-class imbalance problem

2. The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis

3. Boosted Near-miss Under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets

4. New Applications of Ensembles of Classifiers

5. MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning

Cited by 4 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. A majority affiliation based under-sampling method for class imbalance problem;Information Sciences;2024-03

2. IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data;Complexity;2023-11-11

3. Imboost: A New Weighting Factor for Boosting to Handle Imbalanced Problems;2023

4. A Majority Affiliation Based Under-Sampling Method for Class Imbalance Problem;2023

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