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dc.identifier.citationTanti, M. (2019). Ensemble methods to adjust for concept drift in credit card fraud detection (Master's dissertation).en_GB
dc.description.abstractMachine Learning systems are gaining a lot of popularity due to their effectiveness in multiple domains. However, the assumption of stationary in the data, taken in research, can hinder the ability for such methods to find their way into production systems, as it is very common that real world systems and data streams see their underlying distributions change (characterized as Concept Drift), making the trained system become obsolete over time. Credit card fraud in online transactions is one such area where this drift is prevalent, which creates a major problem in a number of different industrial sectors, with millions being lost every year. This work addresses the various problems that arise when trying to fight fraud in a large scale over a long period of time. An extension to the Accuracy Weighted Ensemble is proposed, with the extension consisting of a diversity maximized ensemble that holds classifiers from previous concepts. This new ensemble is used to transfer knowledge, using weight transfer in Multi-Layer Perceptron (MLP), to inform the training of new classifiers being trained at every update stage. The proposed system is evaluated and compared to 3 other popular ensemble models that are robust to concept drift, on a fraud dataset, given by an industrial partner, as well as 4 other benchmark concept drift datasets. The result of this work shows that the proposed ensemble mechanism does provided an edge in performance compared to the other methods tested, while keeping a constant the memory footprint over time.en_GB
dc.subjectCredit card frauden_GB
dc.subjectMachine learningen_GB
dc.subjectNeural networks (Computer science)en_GB
dc.subjectArtificial immune systemsen_GB
dc.titleEnsemble methods to adjust for concept drift in credit card fraud detectionen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Information and Communication Technology. Department of Artificial Intelligenceen_GB
dc.contributor.creatorTanti, Matthew-
Appears in Collections:Dissertations - FacICT - 2019
Dissertations - FacICTAI - 2019

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