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Digital
Library of the European Council for Modelling
and Simulation |
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Title: |
A Novel Oversampling
Technique To Handle Imbalanced Datasets |
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Authors: |
Ayat
Mahmoud, Ayman El-Kilany, Farid Ali, Sherif Mazen |
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Published in:
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2020). ECMS 2020 Proceedings
Edited by: Mike Steglich, Christian Muller, Gaby Neumann, Mathias Walther,
European Council for Modeling and Simulation. DOI: http://doi.org/10.7148/2020 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) ISBN: 978-3-937436-68-5 Communications of the ECMS , Volume 34, Issue 1, June 2020, United Kingdom |
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Citation
format: |
Ayat Mahmoud, Ayman El-Kilany, Farid
Ali, Sherif Mazen (2020). A Novel Oversampling Technique To Handle Imbalanced
Datasets, ECMS 2020 Proceedings Edited By:
Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther European Council
for Modeling and Simulation. doi: 10.7148/2020-0177 |
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DOI: |
https://doi.org/10.7148/2020-0177 |
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Abstract: |
With the
amount of data is growing extensively in different domains in the recent
years, the data imbalance problem arises frequently. A dataset is called
imbalanced when the data of a certain class has significantly more instances
than that of other classes of the same dataset. This imbalanced nature of the
data negatively affects the performance of a classifier since
misclassification of data may cause data analysis results to be inaccurate
and hence leads to wrong business decisions. This paper presents a study of
the different techniques that are used to handle the imbalanced dataset, and
finally proposes a novel oversampling technique to tackle the binary
classification of imbalanced dataset problem. |
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text: |
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