262 citations · 262 across the 4 of their papers we have counts for
5 papers · 1 filter
A data-science pipeline to enable the Interpretability of Many-Objective Feature Selection
Uchechukwu F. Njoku, Alberto Abelló, Besim Bilalli +1
Many-Objective Feature Selection (MOFS) approaches use four or more objectives to determine the relevance of a subset of features in a supervised learning task. As a consequence, M…
Uplift vs. predictive modeling: a theoretical analysis
Théo Verhelst, Robin Petit, Wouter Verbeke +1
Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has…
Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives
Danele Lunghi, Alkis Simitsis, Olivier Caelen +1
Data economy relies on data-driven systems and complex machine learning applications are fueled by them. Unfortunately, however, machine learning models are exposed to fraudulent a…
Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production
Wissam Siblini, Guillaume Coter, Rémy Fabry +5
The dark face of digital commerce generalization is the increase of fraud attempts. To prevent any type of attacks, state-of-the-art fraud detection systems are now embedding Machi…
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization
Fabirzio Carcillo, Yann-Aël Le Borgne, Olivier Caelen +1
Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they…