AccScience Publishing / IJOSI / Volume 9 / Issue 2 / DOI: 10.6977/IJoSI.202504_9(2).0002
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Structure learning of Bayesian networks using sparrow optimization algorithm

Shahab Wahhab Kareem1,2* ,  Hoshang Qasim Awla3 ,  Amin Salih Mohammed4
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1 Department of Technical Information Systems Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq
2 Department of Computer Technical Engineering, Al-Qalam University College, Kirkuk, Iraq
3 Department of Computer Science, Faculty of Science, Soran University, Soran, Iraq
4 Department of Software and Informatics, Salahaddin University, Erbil, Iraq
IJOSI 2025 , 9(2), 14–25; https://doi.org/10.6977/IJoSI.202504_9(2).0002
Submitted: 12 June 2024 | Revised: 10 December 2024 | Accepted: 19 December 2024 | Published: 8 April 2025
© 2025 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones; F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use.

Keywords
Search and Score
Global and Local Search
Bayesian Network
Sparrow Search Optimization Algorithm
Structure Learning
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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