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random undersampling

Description: Random undersampling is a data-balancing method that randomly removes observations from majority classes to reduce class imbalance in predictive modeling.
OpenAlex ID: keywords/random-undersampling
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
2011 · M. Galar, A. Fernandez, et al. · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · 2,799
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
2009 · Chris Seiffert, Taghi M. Khoshgoftaar, et al. · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · 1,867
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
2013 · Mikel Galar, Alberto Fernández, et al. · Pattern Recognition · 419
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
2022 · Ruben van den Goorbergh, Maarten van Smeden, et al. · Journal of the American Medical Informatics Association · 365
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Foundations of data imbalance and solutions for a data democracy
2020 · Ajay Kulkarni, Deri Chong, et al. · Elsevier eBooks · 354
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A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data
2021 · Matloob Khushi, Kamran Shaukat, et al. · IEEE Access · 332
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RUSBoost: Improving classification performance when training data is skewed
2008 · Chris Seiffert, Taghi M. Khoshgoftaar, et al. · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 313
Addressing imbalance in multilabel classification: Measures and random resampling algorithms
2015 · Francisco Charte, Antonio J. Rivera, et al. · Neurocomputing · 288
RETRACTED ARTICLE: Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection
2020 · Fayaz Itoo, Meenakshi, et al. · International Journal of Information Technology · 250
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
2017 · Xinli Yang, David Lo, et al. · Information and Software Technology · 216
Knowledge discovery from imbalanced and noisy data
2009 · Jason Van Hulse, Taghi Khoshgoftaar · Data & Knowledge Engineering · 202
Fast dynamic 3D MR spectroscopic imaging with compressed sensing and multiband excitation pulses for hyperpolarized 13C studies
2010 · Peder E. Z. Larson, Simon Hu, et al. · Magnetic Resonance in Medicine · 192
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GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning
2021 · Carmen Esposito, Gregory A. Landrum, et al. · Journal of Chemical Information and Modeling · 190
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Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework
2022 · Petr Hajek, Mohammad Zoynul Abedin, et al. · Information Systems Frontiers · 180
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Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
2018 · Abhishek Divekar, Meet Parekh, et al. · 175
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Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data
2015 · Joseph Prusa, Taghi M. Khoshgoftaar, et al. · IEEE International Conference on Information Reuse and Integration (IRI) · 162
Pipe failure modelling for water distribution networks using boosted decision trees
2018 · Daniel Winkler, Markus Haltmeier, et al. · Structure and Infrastructure Engineering · 156
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A new sampling method for classifying imbalanced data based on support vector machine ensemble
2016 · Chuanxia Jian, Jian Gao, et al. · Neurocomputing · 153
The Effects of Over and Under Sampling on Fault-prone Module Detection
2007 · Yasutaka Kamei, Akito Monden, et al. · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · 153
The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis
2016 · M. Bach, A. Werner, et al. · Information Sciences · 149
Designing Template-Free Predictor for Targeting Protein-Ligand Binding Sites with Classifier Ensemble and Spatial Clustering
2013 · Dong-Jun Yu, Jun Hu, et al. · IEEE Transactions on Computational Biology and Bioinformatics · 149
Dealing with class imbalance in classifier chains via random undersampling
2019 · Bin Liu, Grigorios Tsoumakas · Knowledge-Based Systems · 148
Missing samples analysis in signals for applications to L-estimation and compressive sensing
2013 · Ljubisa Stankovic, Srdjan Stankovic, et al. · Signal Processing · 148
Classification of Imbalance Data using Tomek Link (T-Link) Combined with Random Under-sampling (RUS) as a Data Reduction Method
2016 · Elhassan AT, Aljourf M, et al. · Global Journal of Technology and Optimization · 139
Intrusion Detection System After Data Augmentation Schemes Based on the VAE and CVAE
2022 · Chang Liu, Ruslan Antypenko, et al. · IEEE Transactions on Reliability · 133
Works count: 1,496
Citation count: 24,190
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