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Cost-sensitive thresholding over a two-dimensional decision region for fraud detection
dc.contributor.author | C-Rella, Jorge | |
dc.contributor.author | Cao, Ricardo | |
dc.contributor.author | Vilar, Juan M. | |
dc.date.accessioned | 2024-04-17T08:11:06Z | |
dc.date.available | 2024-04-17T08:11:06Z | |
dc.date.issued | 2024-02 | |
dc.identifier.citation | J. C-Rella, R. Cao, y J. M. Vilar, «Cost-sensitive thresholding over a two-dimensional decision region for fraud detection», Information Sciences, vol. 657, p. 119956, feb. 2024, doi: 10.1016/j.ins.2023.119956 | es_ES |
dc.identifier.issn | 0020-0255 | |
dc.identifier.issn | 1872-6291 | |
dc.identifier.uri | http://hdl.handle.net/2183/36233 | |
dc.description.abstract | [Absctract]: Credit fraud poses a challenging task in terms of detection. It can result in significant losses depending on the amount, so a cost-sensitive perspective needs to be taken. Classical approaches focus on estimating the probability of fraud and selecting a decision threshold, but they often fail to consider the transaction amount or account for the cumulative losses incurred within the sample. Consequently, these approaches can result in sub-optimal strategies. A new thresholding approach is proposed, based on the construction of a two-dimensional decision space with an estimated probability and the credit amount. This expansion allows more freedom for the optimal classification rule search, which is performed with a new algorithm. The proposed method generalizes previous approaches, so an improvement is consistently achieved. In addition, it allows a restricted search. This is shown in a study of two real data sets, comparing the results obtained by a wide range of classifiers. | es_ES |
dc.description.sponsorship | This research has been supported by MICINN Grant PID2020-113578RB-I00 and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema Universitario de Galicia ED431G 2019/01), all of them through the European Regional Development Fund (ERDF). The first author was financed by the Axencia Galega de Innovación Grant 14-IN606D-2021-2607768. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020/14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier B.V. | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/MÉTODOS ESTADÍSTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORÍA Y APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.ins.2023.119956 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Cost-sensitive classification | es_ES |
dc.subject | Instance-dependent classification | es_ES |
dc.subject | Thresholding | es_ES |
dc.subject | Fraud detection | es_ES |
dc.subject | Risk analysis | es_ES |
dc.subject | Decision region | es_ES |
dc.title | Cost-sensitive thresholding over a two-dimensional decision region for fraud detection | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Information Sciences | es_ES |
UDC.volume | 657 | es_ES |
UDC.startPage | 119956 | es_ES |
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