About twenty-five naughty entropies in belief function theory: Do they measure informativeness?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00635531" target="_blank" >RIV/67985556:_____/25:00635531 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61384399:31160/25:00061251

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0888613X25000957?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0888613X25000957?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijar.2025.109454" target="_blank" >10.1016/j.ijar.2025.109454</a>

Jazyk výsledku

angličtina

Název v původním jazyce

About twenty-five naughty entropies in belief function theory: Do they measure informativeness?

Popis výsledku v původním jazyce

This paper addresses the long-standing challenge of identifying belief function entropies that can effectively guide model learning within the Dempster-Shafer theory of evidence. Building on the analogy with classical probabilistic approaches, we examine 25 entropy functions documented in the literature and evaluate their potential to define mutual information in the belief function framework. As conceptualized in probability theory, mutual information requires strictly subadditive entropies, which are inversely related to the informativeness of belief functions. After extensive analysis, we have found that none of the studied entropy functions fully satisfy these criteria. Nevertheless, certain entropy functions exhibit properties that may make them useful for heuristic model learning algorithms. This paper provides a detailed comparative study of these functions, explores alternative approaches using divergence-based measures, and offers insights into the design of information-theoretic tools for belief function models.

Název v anglickém jazyce

About twenty-five naughty entropies in belief function theory: Do they measure informativeness?

Popis výsledku anglicky

This paper addresses the long-standing challenge of identifying belief function entropies that can effectively guide model learning within the Dempster-Shafer theory of evidence. Building on the analogy with classical probabilistic approaches, we examine 25 entropy functions documented in the literature and evaluate their potential to define mutual information in the belief function framework. As conceptualized in probability theory, mutual information requires strictly subadditive entropies, which are inversely related to the informativeness of belief functions. After extensive analysis, we have found that none of the studied entropy functions fully satisfy these criteria. Nevertheless, certain entropy functions exhibit properties that may make them useful for heuristic model learning algorithms. This paper provides a detailed comparative study of these functions, explores alternative approaches using divergence-based measures, and offers insights into the design of information-theoretic tools for belief function models.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Approximate Reasoning

ISSN

0888-613X

e-ISSN

1873-4731

Svazek periodika

184

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

109454

Kód UT WoS článku

001485002800001

EID výsledku v databázi Scopus

2-s2.0-105003665803