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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.

Journal Article Type Article
Journal International journal of neural systems
Print ISSN 0129-0657
Electronic ISSN 1793-6462
Publisher World Scientific Publishing
Peer Reviewed Peer Reviewed
Institution 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], (accepted). Available from: https://doi.org/10.1142/S012906572050043
DOI https://doi.org/10.1142/S012906572050043
Keywords Class overlap; Imbalanced data; Undersampling; Classification; Adaptive threshold; Fuzzy C-means; Epilepsy; Parkinson's Disease
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