Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results 1

By Salvatore J. Stolfo, David W. Fan, Wenke Lee, Andreas L. Prodromidis, Philip Ka-Fai Chan. 1997

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Citations in the corpus

Differentially Private Meta-Learning (2019, background): “Examples of this include learning models for next-word prediction on cell phone data (McMahan et al., 2018), clinical predictions using hospital records (Zhang et al., 2019), and fraud detectors for competing credit card companies (Stolfo et al., 1997).”
A Survey of Methods for Scaling Up Inductive Algorithms (1999, background): “Stolfo et al. present their approach and an implemented system, and discuss how privacy concerns restrict the federation of banking data (Stolfo et al., 1997).”
Credit card fraud detection using machine learning techniques: A comparative analysis (2017, methodology): “A random sampling approach is used in [18, 20] and reports experimental results indicating that 50:50 artificially distribution of fraud/non-fraud training data generate classifiers with the highest true positive rate and low false positive rate.”
A survey on instance selection for active learning (2012, unknown): “– Fraud Detection [50].”
Credit Card Fraud Detection Using Hidden Markov Model (2012, methodology): “[6] suggest a credit card fraud detection system (FDS) using metalearning techniques to learn models of fraudulent credit card transactions.”
Fraud detection system: A survey (2016, background): “In a supervised learning approach, the class imbalance problem happens when the minority class is very small, leading to numerous problems such as disability of learners to discover patterns in the minority class data (Stolfo et al., 1997).”
Mining concept-drifting data streams using ensemble classifiers (2003, background): “A detailed description of this data set can be found in [26].”