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

Jiajing Huang, Teng Li, Yanzhe Xu, Teresa Wu, Hyunsoo Yoon, Jennifer R. Charlton, Kevin M. Bennett

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationIISE Annual Conference and Expo 2022
EditorsK. Ellis, W. Ferrell, J. Knapp
PublisherInstitute of Industrial and Systems Engineers, IISE
ISBN (Electronic)9781713858072
Publication statusPublished - 2022
EventIISE Annual Conference and Expo 2022 - Seattle, United States
Duration: 2022 May 212022 May 24

Publication series

NameIISE Annual Conference and Expo 2022


ConferenceIISE Annual Conference and Expo 2022
Country/TerritoryUnited States

Bibliographical note

Funding Information:
We gratefully thank DOE CYDRES Project (Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)), NSF-PFI (PFI-RP #1827757: Data-Driven Services for High Performance and Sustainable Buildings) and NIH-R01 (R01DK111861: Comprehensive MRI-based Evaluation of Human Renal Microstructure) for support for this work.

Publisher Copyright:
© 2022 IISE Annual Conference and Expo 2022. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning'. Together they form a unique fingerprint.

Cite this