Creator:

Kałuża, Daniel ; Janusz, Andrzej ; Ślęzak, Dominik

Contributor:

Campagner, Andrea - ed. ; Lenz, Oliver Urs - ed. ; Xia, Shuyin - ed.

Title:

Evidence-theoretical modeling of uncertainty induced by posterior probability distributions

Subtitle:

.

Group publication title:

AMCS, volume 35 (2025)

Subject and Keywords:

theory of evidence ; posterior probabilities ; measures of uncertainty ; active learning

Abstract:

We discuss how the posterior probability distributions produced by machine learning models for analyzed objects can be transformed into evidence-theoretical mass functions that model uncertainties associated with operating those distributions. We investigate the mathematical properties of the introduced mass functions and their corresponding belief functions. We also construct some uncertainty measures based on the functions considered and compare them with several classical uncertainty measures, both theoretically and practically, in the active learning scenarios.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2025

Resource Type:

artykuł

DOI:

10.61822/amcs-2025-0003

Pages:

33-43

Source:

AMCS, volume 35, number 1 (2025) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego