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Anomaly Detection, Consider Your Dataset First An Illustration on Fraud Detection.

, , , , , and . ICTAI, page 1351-1355. IEEE, (2019)

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NAG: Neural feature aggregation framework for credit card fraud detection., , , , and . ICDM, page 92-101. IEEE, (2020)CRAFTML, une forêt aléatoire efficace pour l'apprentissage multi-label extrême., , and . EGC, volume E-35 of RNTI, page 383-388. Éditions RNTI, (2019)Anomaly Detection, Consider Your Dataset First An Illustration on Fraud Detection., , , , , and . ICTAI, page 1351-1355. IEEE, (2019)Multilingual Question Answering from Formatted Text applied to Conversational Agents., , , and . CoRR, (2019)Efficient Open Domain Question Answering With Delayed Attention in Transformer-Based Models., , and . Int. J. Data Warehous. Min., 18 (2): 1-16 (2022)Système de question-réponse multilingue appliqué aux agents conversationnels., , , and . EGC, volume E-36 of RNTI, page 333-340. Éditions RNTI, (2020)Incremental learning strategies for credit cards fraud detection: Extended abstract., , , , , and . DSAA, page 785-786. IEEE, (2020)A Review on Dimensionality Reduction for Multi-Label Classification., , and . IEEE Trans. Knowl. Data Eng., 33 (3): 839-857 (2021)The Importance of Future Information in Credit Card Fraud Detection., , , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 10067-10077. PMLR, (2022)Apprentissage multi-label extrême: Comparaisons d'approches et nouvelles propositions. (Extreme multi-label learning: comparisons of approaches and new proposals).. École Polytechnique, Palaiseau, France, (2018)