2026-04-15T19:45:58Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/4610 2022-03-24T23:12:45Z 1582963302567:1597824273898 user-ucsy

Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree Khine, Aye Aye Khin, Hnin Wint 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. 2020-02-28 http://hdl.handle.net/20.500.12678/0000004610 https://meral.edu.mm/records/4610