Feature Selection in Imbalanced Data
| dc.contributor.author | Kamalov, Firuz | |
| dc.contributor.author | Thabtah, Fadi | |
| dc.contributor.author | Leung, Ho Hon | |
| dc.date.accessioned | 2022-02-16T15:29:29Z | |
| dc.date.available | 2022-02-16T15:29:29Z | |
| dc.date.copyright | © 2021 | |
| dc.date.issued | 2023-12 | |
| dc.description.abstract | The traditional feature selection methods are not suitable for imbalanced data as they tend to be biased towards the majority class. This problem is particularly acute in the field of medical diagnostics and fraud detection where the class distribution is highly skewed. In this paper, we propose a novel filter approach using decision tree-based F1-score. The F1-score incorporates the accuracy with respect to the minority class data and hence is a good measure in the case of imbalanced data. In the proposed implementation, the F1-score is calculated based on a 1-dimensional decision tree classifier resulting in a fast and effective feature evaluation method. Numerical experiments confirm that the proposed method achieves robust dimensionality reduction and accuracy results. In addition, the low computational complexity of the algorithm makes it a practical choice for big data applications. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. | en_US |
| dc.identifier.citation | Kamalov, F., Thabtah, F., & Leung, H. H. (2023). Feature selection in imbalanced data. Annals of Data Science, 10(6), 1527-1541. https://doi.org/10.1007/s40745-021-00366-5 | en_US |
| dc.identifier.issn | 21985804 | |
| dc.identifier.uri | https://doi.org/10.1007/s40745-021-00366-5 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12519/514 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
| dc.relation | Authors Affiliations : Kamalov, F., Canadian University of Dubai, Dubai, United Arab Emirates; Thabtah, F., Manukau Institute of Technology, Manukau, New Zealand; Leung, H.H., UAE University, Al Ain, United Arab Emirates | |
| dc.relation.ispartofseries | Annals of Data Science; | |
| dc.rights | License to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center. | |
| dc.rights.holder | Copyright : © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. | |
| dc.subject | Big data | en_US |
| dc.subject | Data mining | en_US |
| dc.subject | F1-score | en_US |
| dc.subject | Feature selection | en_US |
| dc.subject | Filter method | en_US |
| dc.subject | Imbalanced data | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | Feature Selection in Imbalanced Data | en_US |
| dc.type | Article | en_US |
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