SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models

Research article (Algorithms, 2025) · cited 66× · AI/ML
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SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models

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SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models. Retrieved May 24, 2026, from https://4ort.xyz/entity/smote-vs-smoteenn-a-study-on-the-performance-of-resampling-algorithms-for-addressing-class-imbalance-in-regression-model
MLA “SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/smote-vs-smoteenn-a-study-on-the-performance-of-resampling-algorithms-for-addressing-class-imbalance-in-regression-model.
BibTeX @misc{4ortxyz_smote-vs-smoteenn-a-study-on-the-performance-of-resampling-algorithms-for-addressing-class-imbalance-in-regression-model_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models}}, year = {2026}, url = {https://4ort.xyz/entity/smote-vs-smoteenn-a-study-on-the-performance-of-resampling-algorithms-for-addressing-class-imbalance-in-regression-model}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models — https://4ort.xyz/entity/smote-vs-smoteenn-a-study-on-the-performance-of-resampling-algorithms-for-addressing-class-imbalance-in-regression-model (retrieved 2026-05-24)

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