Customs Fraud Detection in the Presence of Concept Drift

Autor:	Mai, Tung-Duong, Hoang, Kien, Baigutanova, Aitolkyn, Alina, Gaukhartas, Kim, Sundong
Rok vydání:	2021
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	Capturing the changing trade pattern is critical in customs fraud detection. As new goods are imported and novel frauds arise, a drift-aware fraud detection system is needed to detect both known frauds and unknown frauds within a limited budget. The current paper proposes ADAPT, an adaptive selection method that controls the balance between exploitation and exploration strategies used for customs fraud detection. ADAPT makes use of the model performance trends and the amount of concept drift to determine the best exploration ratio at every time. Experiments on data from four countries over several years show that each country requires a different amount of exploration for maintaining its fraud detection system. We find the system with ADAPT can gradually adapt to the dataset and find the appropriate amount of exploration ratio with high performance. Comment: To be published in ICDM IncrLearn Workshop 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.14155 Zobrazit plný text záznamu View this record from Arxiv