Article

Classifying imbalanced data using a bagging ensemble variation (BEV)

Author:

Cen LiAuthors Info & Claims

ACMSE '07: Proceedings of the 45th annual ACM Southeast Conference

Pages 203 - 208

https://doi.org/10.1145/1233341.1233378

Published: 23 March 2007 Publication History

Abstract

In many applications, data collected are highly skewed where data of one class clearly dominates data from the other classes. Most existing classification systems that perform well on balanced data give very poor performance on imbalanced data, especially for the minority class data. Existing work on improving the quality of classification on imbalanced data include over-sampling, under-sampling, and methods that make modifications to the existing classification systems. This paper discusses the BEV system for classifying imbalanced data. The system is developed based on the ideas from the "Bagging" classification ensemble. The motivation behind the scheme is to maximally use the minority class data without creating synthetic data or making changes to the existing classification systems. Experimental results using real world imbalanced data show the effectiveness of the system.

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Published In

cover image ACM Conferences

ACMSE '07: Proceedings of the 45th annual ACM Southeast Conference

March 2007

574 pages

ISBN:9781595936295

DOI:10.1145/1233341

Conference Chairs:
David John
Wake Forest University
,
Sandria Kerr
Winston-Salem State University

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 March 2007

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ACM SE07

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ACM SE07: ACM Southeast Regional Conference

March 23 - 24, 2007

North Carolina, Winston-Salem

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ACMSE '07 Paper Acceptance Rate 81 of 137 submissions, 59%;

Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Dar AFarooq S(2024)An ensemble model for addressing class imbalance and class overlap in software defect predictionInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02538-x15:12(5584-5603)Online publication date: 9-Nov-2024
https://doi.org/10.1007/s13198-024-02538-x
Dar AFarooq S(2024)Handling class overlap and imbalance using overlap driven under-sampling with balanced random forest in software defect predictionInnovations in Systems and Software Engineering10.1007/s11334-024-00571-4Online publication date: 18-Jun-2024
https://doi.org/10.1007/s11334-024-00571-4
Hijazi BAl-Daker YMahmoud YIsmail HZgheib RKamalov F(2024)The Effect of Imbalanced Data on Machine Learning AlgorithmsInventive Communication and Computational Technologies10.1007/978-981-97-7710-5_69(887-897)Online publication date: 15-Dec-2024
https://doi.org/10.1007/978-981-97-7710-5_69
Ke STsai CPan YLin W(2023)Majority re-sampling via sub-class clustering for imbalanced datasetsJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2023.216571536:8(1581-1596)Online publication date: 10-Jan-2023
https://doi.org/10.1080/0952813X.2023.2165715
Nababan ASutarman Zarlis MNababan E(2023)Improving the accuracy of k-nearest neighbor (k-NN) using Synthetic Minority Oversampling Technique (SMOTE) and Gain Ratio (GR) for imbalanced class data2ND INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION SCIENTIFIC DEVELOPMENT (ICAISD) 2021: Innovating Scientific Learning for Deep Communication10.1063/5.0128413(030012)Online publication date: 2023
https://doi.org/10.1063/5.0128413
Zhuang JCai JZhang JZheng WWang R(2023)Class attention to regions of lesion for imbalanced medical image recognitionNeurocomputing10.1016/j.neucom.2023.126577555:COnline publication date: 28-Oct-2023
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Farooq SDar A(2022)A Survey of Different Approaches for the Class Imbalance Problem in Software Defect PredictionInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.30126814:1(1-26)Online publication date: 3-Jun-2022
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