2 citations · 2 across the 1 of their papers we have counts for
7 papers
Explainable cluster analysis: a bagging approach
Federico Maria Quetti, Elena Ballante, Silvia Figini +1
A major limitation of clustering approaches is their lack of explainability: methods rarely provide insight into which features drive the grouping of similar observations. To addre…
On Rank Graduation Metrics for High Dimensional Ordinal Data
Gennaro Auricchio, Adelaide Emma Bernardelli, Paolo Giudici +1
Evaluating the reliability of machine learning classifications remains a fundamental challenge in Artificial Intelligence (AI), particularly when the target variable is multidimens…
A Multiclass ROC Curve
Paolo Giudici, Rosa C. Rosciano, Johanna Schrader +1
This paper introduces a novel methodology for constructing multiclass ROC curves using the multidimensional Gini index. The proposed methodology leverages the established relations…
From Kinetic Theory to AI: a Rediscovery of High-Dimensional Divergences and Their Properties
Gennaro Auricchio, Giovanni Brigati, Paolo Giudici +1
Selecting an appropriate divergence measure is a critical aspect of machine learning, as it directly impacts model performance. Among the most widely used, we find the Kullback-Lei…
How to measure multidimensional variation?
Gennaro Auricchio, Paolo Giudici, Giuseppe Toscani
The coefficient of variation, which measures the variability of a distribution from its mean, is not uniquely defined in the multidimensional case, and so is the multidimensional G…
Multivariate Gini-type discrepancies
Gennaro Auricchio, Giovanni Brigati, Paolo Giudici +1
Measuring distances in a multidimensional setting is a challenging problem, which appears in many fields of science and engineering. In this paper, to measure the distance between…