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Multiple perspectives HMM-based feature engineering for credit card fraud detection.

, , , , , , и . SAC, стр. 1359-1361. ACM, (2019)

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Другие публикации лиц с тем же именем

A Selecting-the-Best Method for Budgeted Model Selection., и . ECML/PKDD (1), том 6911 из Lecture Notes in Computer Science, стр. 249-262. Springer, (2011)Multiple perspectives HMM-based feature engineering for credit card fraud detection., , , , , , и . SAC, стр. 1359-1361. ACM, (2019)When is Undersampling Effective in Unbalanced Classification Tasks?, , и . ECML/PKDD (1), том 9284 из Lecture Notes in Computer Science, стр. 200-215. Springer, (2015)Calibrating Probability with Undersampling for Unbalanced Classification., , , и . SSCI, стр. 159-166. IEEE, (2015)Combining unsupervised and supervised learning in credit card fraud detection., , , , , и . Inf. Sci., (2021)Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inference., , , , и . BNAIC/BENELEARN (Selected Papers), том 1196 из Communications in Computer and Information Science, стр. 182-200. Springer, (2019)An Adversary Model of Fraudsters' Behavior to Improve Oversampling in Credit Card Fraud Detection., , , и . IEEE Access, (2023)Online Non-linear Gradient Boosting in Multi-latent Spaces., , , , и . IDA, том 11191 из Lecture Notes in Computer Science, стр. 99-110. Springer, (2018)Embeddings of Categorical Variables for Sequential Data in Fraud Context., , и . AMLTA, том 723 из Advances in Intelligent Systems and Computing, стр. 542-552. Springer, (2018)Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives., , , и . DEC@SIGMOD, стр. 27-33. ACM, (2023)