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Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree

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dc.contributor.author Khine, Aye Aye
dc.contributor.author Khin, Hnin Wint
dc.date.accessioned 2020-03-18T05:54:59Z
dc.date.available 2020-03-18T05:54:59Z
dc.date.issued 2020-02-28
dc.identifier.isbn 978-1-7281-5925-6
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2528
dc.description.abstract Nowadays, data stream mining is a very hot and high attention research field due to the real-time industrial applications from different sources are generating amount of data continuously as the streaming style. To process these growing and large data streams, data stream mining, classification algorithms have been proposed. These algorithms have to deal with high processing time and memory costs, class imbalance, overfitting and concept drift and so on. It is sure that ensembles of classifiers are being effectively used to make improvement in the accuracy of single classifiers in either data mining or data stream mining. Thus, to get higher performance in prediction with largely no increasing memory and time costs, this paper proposes an Online Boosting(OLBoost) Approach, which is firstly use the Extremely Fast Decision Tree (EFDT) as base (weak) learner , in order to ensemble them into a single online strong learner. The experiments of the proposed method were carried out for credit card fraud detection domain with the sample benchmark datasets. en_US
dc.language.iso en en_US
dc.publisher Proceedings of the Eighteenth International Conference On Computer Applications (ICCA 2020) en_US
dc.subject EFDT en_US
dc.subject Boosting en_US
dc.subject Credit Card Fraud en_US
dc.subject Data Stream Mining en_US
dc.title Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree en_US
dc.type Article en_US


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