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Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson's disease.

Vuttipittayamongkol, Pattaramon; Elyan, Eyad

Authors

Pattaramon Vuttipittayamongkol



Abstract

Classification of imbalanced datasets has attracted substantial research interest over the past decades. Imbalanced datasets are common in several domains such as health, finance, security and others. A wide range of solutions to handle imbalanced datasets focus mainly on the class distribution problem and aim at providing more balanced datasets by means of resampling. However, existing literature shows that class overlap has a higher negative impact on the learning process than class distribution. In this paper, we propose overlap-based undersampling methods for maximizing the visibility of the minority class instances in the overlapping region. This is achieved by the use of soft clustering and the elimination threshold that is adaptable to the overlap degree to identify and eliminate negative instances in the overlapping region. For more accurate clustering and detection of overlapped negative instances, the presence of the minority class at the borderline areas is emphasized by means of oversampling. Extensive experiments using simulated and real-world datasets covering a wide range of imbalance and overlap scenarios including extreme cases were carried out. Results show signficant improvement in sensitivity and competitive performance with well-established and state-of-the-art methods.

Citation

VUTTIPITTAYAMONGKOL, P. and ELYAN, E. 2020. Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson's disease. International journal of neural systems [online], 30(8), article ID 2050043. Available from: https://doi.org/10.1142/S0129065720500434

Journal Article Type Article
Acceptance Date May 2, 2020
Online Publication Date Jul 17, 2020
Publication Date Aug 31, 2020
Deposit Date Jun 30, 2020
Publicly Available Date Jul 18, 2021
Journal International journal of neural systems
Print ISSN 0129-0657
Electronic ISSN 1793-6462
Publisher World Scientific Publishing
Peer Reviewed Peer Reviewed
Volume 30
Issue 8
Article Number 2050043
DOI https://doi.org/10.1142/S0129065720500434
Keywords Class overlap; Imbalanced data; Undersampling; Classification; Adaptive threshold; Fuzzy C-means; Epilepsy; Parkinson's Disease
Public URL https://rgu-repository.worktribe.com/output/940589
Related Public URLs https://rgu-repository.worktribe.com/output/969620

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