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A Selecting-the-Best Method for Budgeted Model Selection.

, and . ECML/PKDD (1), volume 6911 of Lecture Notes in Computer Science, page 249-262. Springer, (2011)

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Multiple perspectives HMM-based feature engineering for credit card fraud detection., , , , , , and . SAC, page 1359-1361. ACM, (2019)A Selecting-the-Best Method for Budgeted Model Selection., and . ECML/PKDD (1), volume 6911 of Lecture Notes in Computer Science, page 249-262. Springer, (2011)When is Undersampling Effective in Unbalanced Classification Tasks?, , and . ECML/PKDD (1), volume 9284 of Lecture Notes in Computer Science, page 200-215. Springer, (2015)Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy., , , , and . IEEE Trans. Neural Networks Learn. Syst., 29 (8): 3784-3797 (2018)Machine Learning Techniques to Enable Closed-Loop Control in Anesthesia., , , , and . CBMS, page 696-701. IEEE Computer Society, (2006)Calibrating Probability with Undersampling for Unbalanced Classification., , , and . SSCI, page 159-166. IEEE, (2015)Combining unsupervised and supervised learning in credit card fraud detection., , , , , and . Inf. Sci., (2021)Sequence classification for credit-card fraud detection., , , , , , and . Expert Syst. Appl., (2018)Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs., , , , , , and . Future Gener. Comput. Syst., (2020)Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inference., , , , and . BNAIC/BENELEARN (Selected Papers), volume 1196 of Communications in Computer and Information Science, page 182-200. Springer, (2019)