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On the class overlap problem in imbalanced data classification.

Vuttipittayamongkol, Pattaramon; Elyan, Eyad; Petrovski, Andrei

Authors

Pattaramon Vuttipittayamongkol



Abstract

Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on the performance of learning algorithms. This paper provides detailed critical discussion and objective evaluation of class overlap in the context of imbalanced data and its impact on classification accuracy. First, we present a thorough experimental comparison of class overlap and class imbalance. Unlike previous work, our experiment was carried out on the full scale of class overlap and an extreme range of class imbalance degrees. Second, we provide an in-depth critical technical review of existing approaches to handle imbalanced datasets. Existing solutions from selective literature are critically reviewed and categorised as class distribution-based and class overlap-based methods. Emerging techniques and the latest development in this area are also discussed in detail. Experimental results in this paper are consistent with existing literature and show clearly that the performance of the learning algorithm deteriorates across varying degrees of class overlap whereas class imbalance does not always have an effect. The review emphasises the need for further research towards handling class overlap in imbalanced datasets to effectively improve learning algorithms’ performance.

Citation

VUTTIPITTAYAMONGKOL, P., ELYAN, E. and PETROVSKI, A. 2021. On the class overlap problem in imbalanced data classification. Knowledge-based systems [online], 212, article number 106631. Available from: https://doi.org/10.1016/j.knosys.2020.106631

Journal Article Type Article
Acceptance Date Nov 25, 2020
Online Publication Date Nov 27, 2020
Publication Date Jan 5, 2021
Deposit Date Dec 2, 2020
Publicly Available Date Nov 28, 2021
Journal Knowledge-based systems
Print ISSN 0950-7051
Electronic ISSN 1872-7409
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 212
Article Number 106631
DOI https://doi.org/10.1016/j.knosys.2020.106631
Keywords Imbalanced data; Class overlap; Classification; Evaluation metric; Benchmark
Public URL https://rgu-repository.worktribe.com/output/1000460