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Calibrated explanations for multi-class
Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).ORCID iD: 0000-0003-0274-9026
Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).ORCID iD: 0000-0001-9633-0423
Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).ORCID iD: 0000-0003-0412-6199
2024 (English)In: Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications / [ed] Simone Vantini, Matteo Fontana, Aldo Solari, Henrik Boström & Lars Carlsson, PMLR , 2024, Vol. 230, p. 175-194Conference paper, Published paper (Refereed)
Abstract [en]

Calibrated Explanations is a recently proposed feature importance explanation method providing uncertainty quantification. It utilises Venn-Abers to generate well-calibrated factual and counterfactual explanations for binary classification. In this paper, we extend the method to support multi-class classification. The paper includes an evaluation illustrating the calibration quality of the selected multi-class calibration approach, as well as a demonstration of how the explanations can help determine which explanations to trust.

Place, publisher, year, edition, pages
PMLR , 2024. Vol. 230, p. 175-194
Series
Proceedings of Machine Learning Research ; 230
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-66433OAI: oai:DiVA.org:hj-66433DiVA, id: diva2:1906451
Conference
The 13th Symposium on Conformal and Probabilistic Prediction with Applications, 9-11 September 2024, Politecnico di Milano, Milano, Italy
Projects
PREMACOPAFAIRETIAI
Funder
Knowledge Foundation, 20220187, 20200223, 20230040Available from: 2024-10-17 Created: 2024-10-17 Last updated: 2024-10-17Bibliographically approved

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Löfström, TuweLöfström, HelenaJohansson, Ulf

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