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

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

Department of Industrial Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
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
ISBN (Electronic)	9781713858072
Publication status	Published - 2022
Event	IISE Annual Conference and Expo 2022 - Seattle, United States Duration: 2022 May 21 → 2022 May 24

Publication series

Name	IISE Annual Conference and Expo 2022

Conference

Conference	IISE Annual Conference and Expo 2022
Country/Territory	United States
City	Seattle
Period	22/5/21 → 22/5/24

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

Cite this

@inproceedings{b14c09cb2def4312aa93f47f9ab03897,

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.",

author = "Jiajing Huang and Teng Li and Yanzhe Xu and Teresa Wu and Hyunsoo Yoon and Charlton, {Jennifer R.} and Bennett, {Kevin M.}",

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: {\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",

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",

}

Huang, J, Li, T, Xu, Y, Wu, T, Yoon, H, Charlton, JR & Bennett, KM 2022, EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning. in K Ellis, W Ferrell & J Knapp (eds), IISE Annual Conference and Expo 2022. IISE Annual Conference and Expo 2022, Institute of Industrial and Systems Engineers, IISE, IISE Annual Conference and Expo 2022, Seattle, United States, 22/5/21.

EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning. / Huang, Jiajing; Li, Teng; Xu, Yanzhe et al.
IISE Annual Conference and Expo 2022. ed. / K. Ellis; W. Ferrell; J. Knapp. Institute of Industrial and Systems Engineers, IISE, 2022. (IISE Annual Conference and Expo 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - EE-SMOTE

T2 - IISE Annual Conference and Expo 2022

AU - Huang, Jiajing

AU - Li, Teng

AU - Xu, Yanzhe

AU - Wu, Teresa

AU - Yoon, Hyunsoo

AU - Charlton, Jennifer R.

AU - Bennett, Kevin M.

N1 - 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.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85137181396

T3 - IISE Annual Conference and Expo 2022

BT - IISE Annual Conference and Expo 2022

A2 - Ellis, K.

A2 - Ferrell, W.

A2 - Knapp, J.

PB - Institute of Industrial and Systems Engineers, IISE

Y2 - 21 May 2022 through 24 May 2022

ER -

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

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

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