Structure Learning of Bayesian Network : A Review

Shahab Wahhab Kareem; Farah Qasim Ahmed Alyousuf; Kosrat Ahmad; Roojwan Hawezi; Hoshang Qasim Awla

doi:10.25212/lfu.qzj.7.1.38

Authors

Shahab Wahhab Kareem Department of Technical Information Systems Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq.
Farah Qasim Ahmed Alyousuf Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq.
Kosrat Ahmad Department of Technical Information Systems Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq.
Roojwan Hawezi Department of Technical Information Systems Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region, Iraq.
Hoshang Qasim Awla Department of Computer Science, College of Science, Soran University, Soran, Kurdistan Region, Iraq.

DOI:

https://doi.org/10.25212/lfu.qzj.7.1.38

Keywords:

Bayesian Network (BN), Conditional Independence Test, Structure Learning, Global Search, Local Search, Pigeon Inspired Optimization

Abstract

Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way. "Deleting, reversing, moving, and inserting" is an approach to finding the best answer to the proposition of the problem in the algorithm. In the Enhanced Surface Water Searching Technique, most of the hunting for water is done by elephants during dry seasons. Pigeon Optimization, Simulated Annealing, Greedy Search, and the BDeu metrics are being reviewed in combination to evaluate all these strategies being used to solve this problem. They subjected different data sets to the uncertainty matrix in an investigation to find out which of these approaches performed best. According to the evaluation data, the algorithm shows stronger results and delivers better points. Additionally, this article also represents the structure of the learning processes for Bayesian Networks as well

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