3 citations · 9 across the 8 of their papers we have counts for
13 papers
A new perspective on classification: optimally allocating limited resources to uncertain tasks
Toon Vanderschueren, Bart Baesens, Tim Verdonck +1
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain. In credit c…
To do or not to do: cost-sensitive causal decision-making
Diego Olaya, Wouter Verbeke, Jente Van Belle +1
Causal classification models are adopted across a variety of operational business processes to predict the effect of a treatment on a categorical business outcome of interest depen…
Weight-of-evidence 2.0 with shrinkage and spline-binning
Jakob Raymaekers, Wouter Verbeke, Tim Verdonck
In many practical applications, such as fraud detection, credit risk modeling or medical decision making, classification models for assigning instances to a predefined set of class…
HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks
Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets +1
Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks. Usually, MTL-networks are trained on a composite lo…
Misclassification cost-sensitive ensemble learning: A unifying framework
George Petrides, Wouter Verbeke
Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contributio…
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…