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

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

Class imbalance is a prevalent challenge in machine learning that arises from skewed data distributions in one class over another, causing models to prioritize the majority class and underperform on the minority classes. This bias can significantly undermine accurate predictions in real-world scenar...

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Bibliographic Details
Main Authors: Gazi Husain, Daniel Nasef, Rejath Jose, Jonathan Mayer, Molly Bekbolatova, Timothy Devine, Milan Toma
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Algorithms
Subjects:
class imbalance
SMOTE
SMOTEENN
oversampling
machine learning
Online Access:https://www.mdpi.com/1999-4893/18/1/37
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