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Keyword

SMOTE

Description: A machine-learning oversampling method that creates synthetic examples of minority classes to address class imbalance in classification datasets.
Wikidata ID: wiki/Q139928837
OpenAlex ID: keywords/smote
SMOTE: Synthetic Minority Over-sampling Technique
2002 · N. V. Chawla, K. W. Bowyer, et al. · Journal of Artificial Intelligence Research · 32,521
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Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
2005 · Hui Han, Wen-Yuan Wang, et al. · Lecture notes in computer science · 4,313
A study of the behavior of several methods for balancing machine learning training data
2004 · Gustavo E. A. P. A. Batista, Ronaldo C. Prati, et al. · ACM SIGKDD Explorations Newsletter · 4,207
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
2018 · Alberto Fernandez, Salvador Garcia, et al. · Journal of Artificial Intelligence Research · 2,221
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SMOTEBoost: Improving Prediction of the Minority Class in Boosting
2003 · Nitesh V. Chawla, Aleksandar Lazarevic, et al. · Lecture notes in computer science · 1,738
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
2018 · Georgios Douzas, Fernando Bacao, et al. · Information Sciences · 1,273
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Applying Support Vector Machines to Imbalanced Datasets
2004 · Rehan Akbani, Stephen Kwek, et al. · Lecture notes in computer science · 1,152
SMOTE for high-dimensional class-imbalanced data
2013 · Rok Blagus, Lara Lusa · BMC Bioinformatics · 1,114
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Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem
2009 · Chumphol Bunkhumpornpat, Krung Sinapiromsaran, et al. · Lecture notes in computer science · 1,023
A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance
2019 · Dina Elreedy, Amir F. Atiya · Information Sciences · 758
SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
2014 · José A. Sáez, Julián Luengo, et al. · Information Sciences · 627
Effective data generation for imbalanced learning using conditional generative adversarial networks
2017 · Georgios Douzas, Fernando Bacao · Expert Systems with Applications · 601
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
2022 · Damien Dablain, Bartosz Krawczyk, et al. · IEEE Transactions on Neural Networks and Learning Systems · 594
Diversity analysis on imbalanced data sets by using ensemble models
2009 · Shuo Wang, Xin Yao · IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · 584
A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
2023 · Azal Ahmad Khan, Omkar Chaudhari, et al. · Expert Systems with Applications · 536
SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory
2011 · Enislay Ramentol, Yailé Caballero, et al. · Knowledge and Information Systems · 489
Minority report in fraud detection
2004 · Clifton Phua, Damminda Alahakoon, et al. · ACM SIGKDD Explorations Newsletter · 450
Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network
2020 · Kaiyuan Jiang, Wenya Wang, et al. · IEEE Access · 447
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Incremental Learning of Concept Drift from Streaming Imbalanced Data
2012 · Gregory Ditzler, Robi Polikar · IEEE Transactions on Knowledge and Data Engineering · 406
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks
2021 · Tianxiang Zhao, Xiang Zhang, et al. · ACM International Conference on Web Search and Data Mining (WSDM) · 398
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Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques
2021 · Abid Ishaq, Saima Sadiq, et al. · IEEE Access · 395
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A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
2023 · Dina Elreedy, Amir F. Atiya, et al. · Machine Learning · 392
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A decision support system for heart disease prediction based upon machine learning
2021 · Pooja Rani, Rajneesh Kumar, et al. · Journal of Reliable Intelligent Environments · 391
DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
2011 · Chumphol Bunkhumpornpat, Krung Sinapiromsaran, et al. · Applied Intelligence · 391
Addressing the Class Imbalance Problem in Medical Datasets
2013 · M. Mostafizur Rahman, D. N. Davis · International Journal of Machine Learning and Computing · 386
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Works count: 16,640
Citation count: 179,800
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