7 citations · 10 across the 7 of their papers we have counts for
6 papers · 1 filter
On the Normalization of Confusion Matrices: Methods and Geometric Interpretations
Johan Erbani, Pierre-Edouard Portier, Elod Egyed-Zsigmond +2
The confusion matrix is a standard tool for evaluating classifiers by providing insights into class-level errors. In heterogeneous settings, its values are shaped by two main facto…
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity
Johan Erbani, Sonia Ben Mokhtar, Pierre-Edouard Portier +2
Federated learning (FL) is a machine learning paradigm that enables multiple data holders to collaboratively train a machine learning model without sharing their training data with…
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
Ousmane Touat, Julian Stier, Pierre-Edouard Portier +1
A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating gra…
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Yvan Lucas, Pierre-Edouard Portier, Léa Laporte +4
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated ev…
Dataset shift quantification for credit card fraud detection
Yvan Lucas, Pierre-Edouard Portier, Léa Laporte +4
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However purchase behaviour and fraudster strategies may change over ti…
Multiple perspectives HMM-based feature engineering for credit card fraud detection
Yvan Lucas, Pierre-Edouard Portier, Léa Laporte +4
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated ev…