The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability

The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability

In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, three dimensions are taken into account: agreem...

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Bibliographic Details
Main Authors: Federico Cabitza, Andrea Campagner, Domenico Albano, Alberto Aliprandi, Alberto Bruno, Vito Chianca, Angelo Corazza, Francesco Di Pietto, Angelo Gambino, Salvatore Gitto, Carmelo Messina, Davide Orlandi, Luigi Pedone, Marcello Zappia, Luca Maria Sconfienza
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Applied Sciences
Subjects:
inter-rater agreement
reliability
ground truth
machine learning
MRNet
knee
Online Access:https://www.mdpi.com/2076-3417/10/11/4014

Internet

https://www.mdpi.com/2076-3417/10/11/4014

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