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IJMLC 2020 Vol.10(1): 31-37 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.1.894

AB-SMOTE: An Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification

Hisham Al Majzoub and Islam Elgedawy

Abstract—SMOTE is an oversampling approach previously proposed to solve the imbalanced data binary classification problem. SMOTE managed to improve the classification accuracy, however it needs to generate large number of synthetic instances, which is not efficient in terms of memory and time. To overcome such drawbacks, the Borderline-SMOTE (BSMOTE) is previously proposed to minimize the number of generated synthetic instances by generating such instances based on the borderline between the majority and minority classes. Unfortunately, BSMOTE could not provide big savings regarding the number of generated instances, trading to the classification accuracy. To improve BSMOTE accuracy, this paper proposes an Affinitive Borderline SMOTE (AB-SMOTE) that leverages the BSMOTE, and improves the quality of the generated synthetic data by taking into consideration the affinity of the borderline instances. Experiments’ results show the AB-SOMTE, when compared with BSMOTE, managed to produce the most accurate results in the majority of the test cases adopted in our study.

Index Terms—Affinitive B-SMOTE, borderline-SOMTE, imbalanced data oversampling, SMOTE.

Hisham Al Majzoub is with the Management Information Systems Department, School of Applied Sciences, Cyprus International University Nicosia, via Mersin 10 – Turkey (e-mail: hisham.m@hotmail.it).
Islam Elgedawy is with Computer Engineering Department, Middle East Technical University, Northern Cyprus Campus, 99738, Kalkanlı, Guzelyurt, Mersin 10, Turkey ( e-mail: elgedawy@metu.edu.tr).

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Cite: Hisham Al Majzoub and Islam Elgedawy, "AB-SMOTE: An Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 31-37, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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