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The imbalanced classification problem has become greatest issue in many fields, especially in fraud detection. In fraud detection, the transaction datasets available for training are extremely imbalanced, with fraudulent transaction logs considerably less represented. Meanwhile, the feature information of the fraud samples with better classification capabilities cannot be mined directly by feature learning methods due to too few fraud samples. These significantly reduce the effectiveness ofdoi:10.1109/access.2020.2994327 fatcat:okewn2wb7zhzzfynmr3nm2zdkq