108 citations · 165 across the 21 of their papers we have counts for
23 papers
Beware of "Explanations" of AI
David Martens, Galit Shmueli, Theodoros Evgeniou +14
Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artifici…
Metalearners for Ranking Treatment Effects
Toon Vanderschueren, Wouter Verbeke, Felipe Moraes +1
Efficiently allocating treatments with a budget constraint constitutes an important challenge across various domains. In marketing, for example, the use of promotions to target pot…
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
Bruno Deprez, Toon Vanderschueren, Bart Baesens +2
Money laundering presents a pervasive challenge, burdening society by financing illegal activities. The use of network information is increasingly being explored to effectively com…
Using representation balancing to learn conditional-average dose responses from clustered data
Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets +2
Estimating a unit's responses to interventions with an associated dose, the "conditional average dose response" (CADR), is relevant in a variety of domains, from healthcare to busi…
Timing Process Interventions with Causal Inference and Reinforcement Learning
Hans Weytjens, Wouter Verbeke, Jochen De Weerdt
The shift from the understanding and prediction of processes to their optimization offers great benefits to businesses and other organizations. Precisely timed process intervention…
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Toon Vanderschueren, Alicia Curth, Wouter Verbeke +1
Machine learning (ML) holds great potential for accurately forecasting treatment outcomes over time, which could ultimately enable the adoption of more individualized treatment str…
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke +1
Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast am…
Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field
Christopher Bockel-Rickermann, Tim Verdonck, Wouter Verbeke
The literature on fraud analytics and fraud detection has seen a substantial increase in output in the past decade. This has led to a wide range of research topics and overall litt…
Prescriptive maintenance with causal machine learning
Toon Vanderschueren, Robert Boute, Tim Verdonck +2
Machine maintenance is a challenging operational problem, where the goal is to plan sufficient preventive maintenance to avoid machine failures and overhauls. Maintenance is often…
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…
The foundations of cost-sensitive causal classification
Wouter Verbeke, Diego Olaya, Jeroen Berrevoets +2
Classification is a well-studied machine learning task which concerns the assignment of instances to a set of outcomes. Classification models support the optimization of managerial…
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…
A study of the U.S. domestic air transportation network: Temporal evolution of network topology and robustness from 2001 to 2016
Leonidas Siozos-Rousoulis, Dimitri Robert, Wouter Verbeke
The U.S. air transportation network (ATN) is critical to the mobility and the functioning of the United States. It is thus necessary to ensure that it is well-connected, efficient,…
Autoencoders for strategic decision support
Sam Verboven, Jeroen Berrevoets, Chris Wuytens +2
In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are avail…
Learning to rank for uplift modeling
Floris Devriendt, Tias Guns, Wouter Verbeke
Uplift modeling has effectively been used in fields such as marketing and customer retention, to target those customers that are most likely to respond due to the campaign or treat…
Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture
María Óskarsdóttir, Cristián Bravo, Wouter Verbeke +3
Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational lear…
A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry
Maria Óskarsdóttir, Cristián Bravo, Wouter Verbeke +3
Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference method…
Optimising Individual-Treatment-Effect Using Bandits
Jeroen Berrevoets, Sam Verboven, Wouter Verbeke
Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community. In particular, estimating the ind…
Causal Simulations for Uplift Modeling
Jeroen Berrevoets, Wouter Verbeke
Uplift modeling requires experimental data, preferably collected in random fashion. This places a logistical and financial burden upon any organisation aspiring such models. Once d…