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DC Field | Value | Language |
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dc.contributor.author | Nandi, AK | - |
dc.contributor.author | Randhawa, KK | - |
dc.contributor.author | Chua, HS | - |
dc.contributor.author | Seera, M | - |
dc.contributor.author | Lim, CP | - |
dc.date.accessioned | 2022-01-18T12:18:42Z | - |
dc.date.available | 2022-01-18T12:18:42Z | - |
dc.date.issued | 2022-01-20 | - |
dc.identifier | ORCID iD: Asoke K. Nandi https://orcid.org/0000-0001-6248-2875 | - |
dc.identifier | e0260579 | - |
dc.identifier.citation | Nandi, A.K.et al. (2022) 'Credit card fraud detection using a hierarchical behavior-knowledge space model', PLoS ONE, 17(1), e0260579 , pp. 1-16. doi: 10.1371/journal.pone.0260579. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23965 | - |
dc.description | Data Availability: All relevant benchmark data are within the manuscript, given in references [24], [25], and [26]. Relevant real data records are available from a public repository: https://doi.org/10.6084/m9.figshare.17030138. | - |
dc.description.abstract | Copyright: © 2022 Nandi et al. With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems. | - |
dc.description.sponsorship | Funding: The author(s) received no specific funding for this work. | - |
dc.format.extent | 1 - 16 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | PLoS | en_US |
dc.rights | Copyright: © 2022 Nandi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | classification | en_US |
dc.subject | decision combination algorithms | en_US |
dc.subject | fraud detection | en_US |
dc.subject | multiple neural network systems | en_US |
dc.subject | payment card | en_US |
dc.subject | machine learning | - |
dc.subject | finance | - |
dc.subject | open data | - |
dc.subject | decision trees | - |
dc.subject | deep learning | - |
dc.subject | neural networks | - |
dc.subject | rain | - |
dc.subject | thyroid | - |
dc.title | Credit card fraud detection using a hierarchical behavior-knowledge space model | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1371/journal.pone.0260579 | - |
dc.relation.isPartOf | PLoS ONE | - |
pubs.issue | 1 | - |
pubs.publication-status | Published | - |
pubs.volume | 17 | - |
dc.identifier.eissn | 1932-6203 | - |
dc.rights.holder | Nandi et al. | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
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FullText .pdf | Copyright: © 2022 Nandi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | 1.13 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License