LSMOTE: A link-based Synthetic Minority Oversampling Technique for binary imbalanced datasets-Reference-Cited by-同舟云学术

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LSMOTE: A link-based Synthetic Minority Oversampling Technique for binary imbalanced datasets

Published:2024-12 Issue: Volume:608 Page:128372
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Cai Qin-Nan,Zhang Zhong-Liang,Wu Yu-Heng^ORCID,Zhang Xiu-Ming

Publisher

Elsevier BV

Reference50 articles.

1. Bilstm deep neural network model for imbalanced medical data of iot systems;Woźniak;Future Gener. Comput. Syst.,2023

2. Class-imbalanced dynamic financial distress prediction based on adaboost-svm ensemble combined with smote and time weighting;Sun;Inf. Fusion,2020

3. An online continual object detector on vhr remote sensing images with class imbalance;Chen;Eng. Appl. Artif. Intell.,2023

4. A systematic review on imbalanced learning methods in intelligent fault diagnosis;Ren;IEEE Trans. Instrum. Meas.,2023

5. An efficient fraud detection framework with credit card imbalanced data in financial services;Abd El-Naby;Multimedia Tools Appl.,2023

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