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.
|Title of host publication||IISE Annual Conference and Expo 2022|
|Editors||K. Ellis, W. Ferrell, J. Knapp|
|Publisher||Institute of Industrial and Systems Engineers, IISE|
|Publication status||Published - 2022|
|Event||IISE Annual Conference and Expo 2022 - Seattle, United States|
Duration: 2022 May 21 → 2022 May 24
|Name||IISE Annual Conference and Expo 2022|
|Conference||IISE Annual Conference and Expo 2022|
|Period||22/5/21 → 22/5/24|
Bibliographical noteFunding 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.
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Industrial and Manufacturing Engineering