4 papers
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Sebastiaan Höppner, Bart Baesens, Wouter Verbeke +1
Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing fraud detection syste…
robROSE: A robust approach for dealing with imbalanced data in fraud detection
Bart Baesens, Sebastiaan Höppner, Irene Ortner +1
A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. In most data sets, frau…
Cellwise Robust M Regression
Peter Filzmoser, Sebastiaan Höppner, Irene Ortner +2
The cellwise robust M regression estimator is introduced as the first estimator of its kind that intrinsically yields both a map of cellwise outliers consistent with the linear mod…
Profit Driven Decision Trees for Churn Prediction
Sebastiaan Höppner, Eugen Stripling, Bart Baesens +2
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of pred…