Structural learning of mixed noisy-OR Bayesian networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00581903" target="_blank" >RIV/67985556:_____/23:00581903 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15210/23:73620687

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0888613X23001214?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0888613X23001214?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijar.2023.108990" target="_blank" >10.1016/j.ijar.2023.108990</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Structural learning of mixed noisy-OR Bayesian networks

Popis výsledku v původním jazyce

In this paper we discuss learning Bayesian networks whose conditional probability tables are either Noisy-OR models or general conditional probability tables. We refer to these models as Mixed Noisy-OR Bayesian Networks. To learn their structure, we modify the Bayesian Information Criterion used for standard Bayesian networks to reflect the number of parameters of a Noisy-OR model. We prove that the log-likelihood function of a Noisy-OR model has a unique maximum and adapt the EM-learning method for the leaky Noisy-OR model. We propose a structure learning algorithm that learns optimal Mixed Noisy-OR Bayesian Networks. We evaluate the proposed approach on synthetic data, where it performs substantially better than standard Bayesian networks. We perform experiments with Bipartite Noisy-OR Bayesian networks of different complexity to find out when the results of Mixed Noisy-OR Bayesian Networks are significantly better than the results of standard Bayesian networks and when they perform similarly. We also study how different penalties based on the number of model parameters affect the quality of the results. Finally, we apply the suggested approach to a problem from the domain of linguistics. Specifically, we use Mixed Noisy-OR Bayesian Networks to model the spread of loanwords in the South-East Asian Archipelago. We perform numerical experiments in which we compare the prediction ability of standard Bayesian networks with Mixed Noisy-OR Bayesian networks and test different pruning methods to reduce the number of parent sets considered.

Název v anglickém jazyce

Structural learning of mixed noisy-OR Bayesian networks

Popis výsledku anglicky

In this paper we discuss learning Bayesian networks whose conditional probability tables are either Noisy-OR models or general conditional probability tables. We refer to these models as Mixed Noisy-OR Bayesian Networks. To learn their structure, we modify the Bayesian Information Criterion used for standard Bayesian networks to reflect the number of parameters of a Noisy-OR model. We prove that the log-likelihood function of a Noisy-OR model has a unique maximum and adapt the EM-learning method for the leaky Noisy-OR model. We propose a structure learning algorithm that learns optimal Mixed Noisy-OR Bayesian Networks. We evaluate the proposed approach on synthetic data, where it performs substantially better than standard Bayesian networks. We perform experiments with Bipartite Noisy-OR Bayesian networks of different complexity to find out when the results of Mixed Noisy-OR Bayesian Networks are significantly better than the results of standard Bayesian networks and when they perform similarly. We also study how different penalties based on the number of model parameters affect the quality of the results. Finally, we apply the suggested approach to a problem from the domain of linguistics. Specifically, we use Mixed Noisy-OR Bayesian Networks to model the spread of loanwords in the South-East Asian Archipelago. We perform numerical experiments in which we compare the prediction ability of standard Bayesian networks with Mixed Noisy-OR Bayesian networks and test different pruning methods to reduce the number of parent sets considered.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/GA20-18407S" target="_blank" >GA20-18407S: Automatizace analýzy slovesných tříd pro ohrožené jazyky - RoboCorp</a><br>

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 Approximate Reasoning

ISSN

0888-613X

e-ISSN

1873-4731

Svazek periodika

161

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

108990

Kód UT WoS článku

001064515700001

EID výsledku v databázi Scopus

2-s2.0-85166914975