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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
Alberto Fernández, Salvador García, Francisco Herrera, et al.
Journal of Artificial Intelligence Research (2018) Vol. 61, pp. 863-905
Open Access | Times Cited: 1533

Showing 1-25 of 1533 citing articles:

AI applications to medical images: From machine learning to deep learning
Isabella Castiglioni, Leonardo Rundo, Marina Codari, et al.
Physica Medica (2021) Vol. 83, pp. 9-24
Open Access | Times Cited: 475
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook
Ricardo Silva Peres, Xiaodong Jia, Jay Lee, et al.
IEEE Access (2020) Vol. 8, pp. 220121-220139
Open Access | Times Cited: 432
A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, et al.
(2021)
Open Access | Times Cited: 358
Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting
Jie Sun, Hui Li, Hamido Fujita, et al.
Information Fusion (2019) Vol. 54, pp. 128-144
Closed Access | Times Cited: 284
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla
IEEE Transactions on Neural Networks and Learning Systems (2022) Vol. 34, Iss. 9, pp. 6390-6404
Open Access | Times Cited: 269
An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets
György Kovács
Applied Soft Computing (2019) Vol. 83, pp. 105662-105662
Closed Access | Times Cited: 235
Time Series Data Augmentation for Deep Learning: A Survey
Qingsong Wen, Liang Sun, Fan Yang, et al.
(2021), pp. 4653-4660
Open Access | Times Cited: 217
Credit Card Fraud Detection - Machine Learning methods
Dejan Varmedja, Mirjana Karanovic, Srdjan Sladojević, et al.
(2019), pp. 1-5
Closed Access | Times Cited: 215
Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE
Georgios Douzas, Fernando Bação
Information Sciences (2019) Vol. 501, pp. 118-135
Open Access | Times Cited: 208
A systematic literature review of methods and datasets for anomaly-based network intrusion detection
Zhen Yang, Xiaodong Liu, Tong Li, et al.
Computers & Security (2022) Vol. 116, pp. 102675-102675
Open Access | Times Cited: 186
Generative Adversarial Minority Oversampling
Sankha Subhra Mullick, Shounak Datta, Swagatam Das
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Open Access | Times Cited: 185
A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data
Joffrey L. Leevy, Taghi M. Khoshgoftaar
Journal Of Big Data (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 170
Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model
Vaibhav Rupapara, Furqan Rustam, Hina Fatima Shahzad, et al.
IEEE Access (2021) Vol. 9, pp. 78621-78634
Open Access | Times Cited: 162
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
Ruben van den Goorbergh, Maarten van Smeden, D. Timmerman, et al.
Journal of the American Medical Informatics Association (2022) Vol. 29, Iss. 9, pp. 1525-1534
Open Access | Times Cited: 162
Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
Rahul Kumar, Ridhi Arora, Vipul Bansal, et al.
medRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 155
A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM
Jingmei Liu, Yuanbo Gao, Fengjie Hu
Computers & Security (2021) Vol. 106, pp. 102289-102289
Closed Access | Times Cited: 149
RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem
Paria Soltanzadeh, Mahdi Hashemzadeh
Information Sciences (2020) Vol. 542, pp. 92-111
Closed Access | Times Cited: 140
Data Augmentation and Intelligent Fault Diagnosis of Planetary Gearbox Using ILoFGAN Under Extremely Limited Samples
Mingzhi Chen, Haidong Shao, Haoxuan Dou, et al.
IEEE Transactions on Reliability (2022) Vol. 72, Iss. 3, pp. 1029-1037
Open Access | Times Cited: 129
A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 127
A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra
Expert Systems with Applications (2023) Vol. 244, pp. 122778-122778
Open Access | Times Cited: 116
capAI - A Procedure for Conducting Conformity Assessment of AI Systems in Line with the EU Artificial Intelligence Act
Luciano Floridi, Matthias Holweg, Mariarosaria Taddeo, et al.
SSRN Electronic Journal (2022)
Closed Access | Times Cited: 112
FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification
Sebastián Maldonado, Carla Vairetti, Alberto Fernández, et al.
Pattern Recognition (2021) Vol. 124, pp. 108511-108511
Open Access | Times Cited: 107
Research on expansion and classification of imbalanced data based on SMOTE algorithm
Shujuan Wang, Yuntao Dai, Jihong Shen, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 107
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
Dina Elreedy, Amir F. Atiya, Firuz Kamalov
Machine Learning (2023) Vol. 113, Iss. 7, pp. 4903-4923
Open Access | Times Cited: 102
A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis
Zhijun Ren, Tantao Lin, Ke Feng, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-35
Closed Access | Times Cited: 88
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