@inproceedings{0b824105f5ba4860848204aaaae400ed,
title = "EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning",
abstract = "Imbalanced learning attracts great attention in various research fields. Existing literature-reported methodologies in imbalanced learning have shown drawbacks including over-generation or noisy/wrong samples generations. This paper presents EE-SMOTE, an oversampling technique based on information entropy, to support the imbalance classifications. Specifically, we propose a metric, Eigen-Entropy (EE), to identify homogenous samples from minority classes for oversampling technique, specifically, SMOTE to reach data balances for classification. Experiments on public dataset and real-world datasets demonstrate the efficacy and effectiveness of the proposed EE-SMOTE in imbalanced learning.",
keywords = "Eigenvalues, Homogeneity, Imbalanced learning, Information entropy, Oversampling",
author = "Jiajing Huang and Teng Li and Yanzhe Xu and Teresa Wu and Hyunsoo Yoon and Charlton, {Jennifer R.} and Bennett, {Kevin M.}",
note = "Publisher Copyright: {\textcopyright} 2022 IISE Annual Conference and Expo 2022. All rights reserved.; IISE Annual Conference and Expo 2022 ; Conference date: 21-05-2022 Through 24-05-2022",
year = "2022",
language = "English (US)",
series = "IISE Annual Conference and Expo 2022",
publisher = "Institute of Industrial and Systems Engineers, IISE",
editor = "K. Ellis and W. Ferrell and J. Knapp",
booktitle = "IISE Annual Conference and Expo 2022",
}