Weighted Cross-Entropy to tackle Overlapping in Fraud Detection - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

Weighted Cross-Entropy to tackle Overlapping in Fraud Detection

Claire Verdier
  • Function : Author
  • PersonId : 1175194
Stephane Perriot
  • Function : Author
Arnault Pachot
  • Function : Author
  • PersonId : 1175890


This paper proposes a new loss function for unbalanced binary data sets with overlapping classes. This loss function is a weighted cross-entropy binary function that takes as its argument the distance between an observation of the majority class and an observation of the nearest minority class. The distance is calculated using the nearest neighbor algorithm. The quality of this loss function is measured with the AUC-ROC metric on a fraud detection data set for energy saving certificates. In addition, the reduction in the workload for human verification is calculated. The new loss function improves the result. A statistical test confirms the improvement of the model using the new loss function.
Fichier principal
Vignette du fichier
ArticleFraudeDetection.pdf (354.87 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03832865 , version 1 (28-10-2022)


  • HAL Id : hal-03832865 , version 1


Claire Verdier, Stephane Perriot, Arnault Pachot. Weighted Cross-Entropy to tackle Overlapping in Fraud Detection. 15th International Conference on Machine Learning and Computing, Feb 2023, Zhuhai, China. ⟨hal-03832865⟩


132 View
43 Download


Gmail Facebook X LinkedIn More