EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning

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 e...

Full description

Saved in:
Bibliographic Details
Published inIISE Annual Conference. Proceedings pp. 1 - 6
Main Authors Huang, Jiajing, Li, Teng, Xu, Yanzhe, Wu, Teresa, Yoon, Hyunsoo, Charlton, Jennifer R, Bennett, Kevin M
Format Journal Article
LanguageEnglish
Published Norcross Institute of Industrial and Systems Engineers (IISE) 01.01.2022
Subjects
Algorithms
Classification
Datasets
Eigenvalues
Entropy
Entropy (Information theory)
Experiments
Industrial engineering
Learning
Machine learning
Methods
Microbalances
Oversampling
Random variables
Online AccessGet full text

Cover

More Information
Summary: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.