A Novel Algorithm for Merging Bayesian Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F23%3A00373687" target="_blank" >RIV/68407700:21260/23:00373687 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/sym15071461" target="_blank" >https://doi.org/10.3390/sym15071461</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/sym15071461" target="_blank" >10.3390/sym15071461</a>
Alternative languages
Result language
angličtina
Original language name
A Novel Algorithm for Merging Bayesian Networks
Original language description
The article presents a novel algorithm for merging Bayesian networks generated by different methods, such as expert knowledge and data-driven approaches, while leveraging a symmetry-based approach. The algorithm combines the strengths of each input network to create a more comprehensive and accurate network. Evaluations on traffic accident data from Prague in the Czech Republic and accidents on railway crossings demonstrate superior predictive performance, as measured by prediction error metric. The algorithm identifies and incorporates symmetric nodes into the final network, ensuring consistent representations across different methods. The merged network, incorporating nodes selected from both the expert and algorithm networks, provides a more comprehensive and accurate representation of the relationships among variables in the dataset. Future research could focus on extending the algorithm to deal with cycles and improving the handling of conditional probability tables. Overall, the proposed algorithm demonstrates the effectiveness of combining different sources of knowledge in Bayesian network modeling.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Symmetry
ISSN
2073-8994
e-ISSN
2073-8994
Volume of the periodical
15
Issue of the periodical within the volume
7
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
—
UT code for WoS article
001069463500001
EID of the result in the Scopus database
2-s2.0-85166225430