Learning the Structure of Bayesian Networks from Incomplete Data Using a Mixture Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00572588" target="_blank" >RIV/67985556:_____/23:00572588 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21340/23:00366181

Výsledek na webu

<a href="https://www.informatica.si/index.php/informatica/article/view/4497" target="_blank" >https://www.informatica.si/index.php/informatica/article/view/4497</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31449/inf.v47i1.4497" target="_blank" >10.31449/inf.v47i1.4497</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning the Structure of Bayesian Networks from Incomplete Data Using a Mixture Model

Popis výsledku v původním jazyce

In this paper, we provide an approach to learning optimal Bayesian network (BN) structures from incomplete data based on the BIC score function using a mixture model to handle missing values. We have compared the proposed approach with other methods. Our experiments have been conducted on different models, some of them Belief Noisy-Or (BNO) ones. We have performed experiments using datasets with values missing completely at random having different missingness rates and data sizes. We have analyzed the significance of differences between the algorithm performance levels using the Wilcoxon test. The new approach typically learns additional edges in the case of Belief Noisy-or models. We have analyzed this issue using the Chi-square test of independence between the variables in the true models, this approach reveals that additional edges can be explained by strong dependence in generated data. An important property of our new method for learning BNs from incomplete data is that it can learn not only optimal general BNs but also specific Belief Noisy-Or models which is using in many applications such as medical application.

Název v anglickém jazyce

Learning the Structure of Bayesian Networks from Incomplete Data Using a Mixture Model

Popis výsledku anglicky

In this paper, we provide an approach to learning optimal Bayesian network (BN) structures from incomplete data based on the BIC score function using a mixture model to handle missing values. We have compared the proposed approach with other methods. Our experiments have been conducted on different models, some of them Belief Noisy-Or (BNO) ones. We have performed experiments using datasets with values missing completely at random having different missingness rates and data sizes. We have analyzed the significance of differences between the algorithm performance levels using the Wilcoxon test. The new approach typically learns additional edges in the case of Belief Noisy-or models. We have analyzed this issue using the Chi-square test of independence between the variables in the true models, this approach reveals that additional edges can be explained by strong dependence in generated data. An important property of our new method for learning BNs from incomplete data is that it can learn not only optimal general BNs but also specific Belief Noisy-Or models which is using in many applications such as medical application.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

International Journal of Computing and Informatics

ISSN

0350-5596

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

SI - Slovinská republika

Počet stran výsledku

14

Strana od-do

83-96

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85158820349