Learned lessons in credit card fraud detection from a practitioner perspective

2014/02/22 by Andrea Dal Pozzolo, Olivier Caelen, Yann‐Aël Le Borgne +3 · 1 citation
Computer Science · #Anomaly Detection Techniques and Applications #Artificial intelligence #Business #Computer science #Computer security #Confidentiality #Credit card #Credit card fraud #Data science #Imbalanced Data Classification Techniques #Key (lock) #Machine Learning and Data Classification #Perspective (graphical) #Risk analysis (engineering) #World Wide Web

paper · doi:10.1016/j.eswa.2014.02.026

openalex publication_date 2014/02/22 · crossref created 2014/02/22 · crossref issued 2014/08/01 · crossref published 2014/08/01 · crossref published-print 2014/08/01 · crossref deposited 2020/08/15 · openalex created_date 2025/10/10 · crossref indexed 2026/07/25 · openalex updated_date 2026/07/26

Citations

• Classifier Ensembles for Changing Environments
• A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
• A streaming ensemble algorithm (SEA) for large-scale classification
• The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
• The CN2 Induction Algorithm
• SMOTE: Synthetic Minority Over-sampling Technique
• The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
• Learning in the presence of concept drift and hidden contexts

Cited by

• Concept drift [wikipedia]