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...

詳細記述

書誌詳細

出版年:	Algorithms
主要な著者:	Gazi Husain, Daniel Nasef, Rejath Jose, Jonathan Mayer, Molly Bekbolatova, Timothy Devine, Milan Toma
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2025-01-01
主題:	class imbalance SMOTE SMOTEENN oversampling machine learning
オンライン･アクセス:	https://www.mdpi.com/1999-4893/18/1/37