262 citations · 432 across the 7 of their papers we have counts for
7 papers
Teaching and learning in the age of artificial intelligence
Margarida Romero, Laurent Heiser, Alexandre Lepage +29
As part of the Digital Working Group (GTnum) #Scol_IA "Renewal of digital practices and creative uses of digital and AI" we are pleased to present the white paper "Teaching and lea…
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…
SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark
Fabrizio Carcillo, Andrea Dal Pozzolo, Yann-Aël Le Borgne +3
The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds i…
Feature selection in high-dimensional dataset using MapReduce
Claudio Reggiani, Yann-Aël Le Borgne, Gianluca Bontempi
This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network…
OpenTED Browser: Insights into European Public Spendings
Yann-Aël Le Borgne, Adriana Homolova, Gianluca Bontempi
We present the OpenTED browser, a Web application allowing to interactively browse public spending data related to public procurements in the European Union. The application relies…
Distributed Principal Component Analysis for Wireless Sensor Networks
Yann-Aël Le Borgne, Sylvain Raybaud, Gianluca Bontempi
The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compressio…