Skip to main content

Research Repository

Advanced Search

CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification.

Elyan, Eyad; Moreno-Garcia, Carlos Francisco; Jayne, Chrisina

Authors

Chrisina Jayne



Abstract

Class-imbalanced datasets are common across several domains such as health, banking, security, and others. The dominance of majority class instances (negative class) often results in biased learning models, and therefore, classifying such datasets requires employing some methods to compact the problem. In this paper, we propose a new hybrid approach aiming at reducing the dominance of the majority class instances using class decomposition and increasing the minority class instances using an oversampling method. Unlike other undersampling methods, which suffer data loss, our method preserves the majority class instances, yet significantly reduces its dominance, resulting in a more balanced dataset and hence improving the results. A large-scale experiment using 60 public datasets was carried out to validate the proposed methods. The results across three standard evaluation metrics show the comparable and superior results with other common and state-of-the-art techniques.

Journal Article Type Article
Journal Neural computing and applications
Print ISSN 0941-0643
Electronic ISSN 1433-3058
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Institution Citation ELYAN, E., MORENO-GARCIA, C.F. and JAYNE, C. 2020. CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification. Neural computing and applications [online], Online First. Available from: https://doi.org/10.1007/s00521-020-05130-z
DOI https://doi.org/10.1007/s00521-020-05130-z
Keywords Machine learning; Class-imbalance; Classification; Undersapmpling; Oversampling

Files





You might also like



Downloadable Citations

;