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Auteurs principaux: Giudici, Paolo, Rosciano, Rosa C., Schrader, Johanna, Kummerfeld, Delf-Magnus
Format: Preprint
Publié: 2025
Sujets:
Accès en ligne:https://arxiv.org/abs/2510.15670
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author Giudici, Paolo
Rosciano, Rosa C.
Schrader, Johanna
Kummerfeld, Delf-Magnus
author_facet Giudici, Paolo
Rosciano, Rosa C.
Schrader, Johanna
Kummerfeld, Delf-Magnus
contents This paper introduces a novel methodology for constructing multiclass ROC curves using the multidimensional Gini index. The proposed methodology leverages the established relationship between the Gini coefficient and the ROC Curve and extends it to multiclass settings through the multidimensional Gini index. The framework is validated by means of two comprehensive case studies in health care and finance. The paper provides a theoretically grounded solution to multiclass performance evaluation, particularly valuable for imbalanced datasets, for which a prudential assessment should take precedence over class frequency considerations.
format Preprint
id arxiv_https___arxiv_org_abs_2510_15670
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Multiclass ROC Curve
Giudici, Paolo
Rosciano, Rosa C.
Schrader, Johanna
Kummerfeld, Delf-Magnus
Methodology
Machine Learning
This paper introduces a novel methodology for constructing multiclass ROC curves using the multidimensional Gini index. The proposed methodology leverages the established relationship between the Gini coefficient and the ROC Curve and extends it to multiclass settings through the multidimensional Gini index. The framework is validated by means of two comprehensive case studies in health care and finance. The paper provides a theoretically grounded solution to multiclass performance evaluation, particularly valuable for imbalanced datasets, for which a prudential assessment should take precedence over class frequency considerations.
title A Multiclass ROC Curve
topic Methodology
Machine Learning
url https://arxiv.org/abs/2510.15670