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

http://hdl.handle.net/20.500.12678/0000004610
76b3d3e7-7b5c-48f1-ba6a-4184700fd0d9
693ce398-1bcb-4d30-8c25-1ccefefe45cd
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Credit Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree.pdf (178 Kb)
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Article
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Publication
Title
Title Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree
Language en
Publication date 2020-02-28
Authors
Khine, Aye Aye
Khin, Hnin Wint
Description
Nowadays, data stream mining is a very hot and highattention research field due to the real-time industrialapplications from different sources are generating amount ofdata continuously as the streaming style. To process thesegrowing and large data streams, data stream mining,classification algorithms have been proposed. Thesealgorithms have to deal with high processing time andmemory costs, class imbalance, overfitting and concept driftand so on. It is sure that ensembles of classifiers are beingeffectively used to make improvement in the accuracy ofsingle classifiers in either data mining or data stream mining.Thus, to get higher performance in prediction with largely noincreasing memory and time costs, this paper proposes anOnline Boosting(OLBoost) Approach, which is firstly use theExtremely Fast Decision Tree (EFDT) as base (weak)learner , in order to ensemble them into a single online stronglearner. The experiments of the proposed method werecarried out for credit card fraud detection domain with thesample benchmark datasets.
Keywords
EFDT, Boosting, Credit Card Fraud, Data Stream Mining
Identifier 978-1-7281-5925-6
Journal articles
Proceedings of the Eighteenth International Conference On Computer Applications (ICCA 2020)
Conference papers
Books/reports/chapters
Thesis/dissertations
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