Falcon optimization algorithm for bayesian network structure learning

• Autorzy: Kareem Shahab Wahhab , Okur Mehmet Cudi

Identyfikatory

DOI

10.7494/csci.2021.22.4.3773

• Języki publikacji: EN

Abstrakty

EN

In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values).

Słowa kluczowe

EN

Bayesian network; global search; falcon optimization algorithm; structure learning; search and score

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2021
• Tom: T. 22 (4)
• Strony: 553--569
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Kareem Shahab Wahhab

• Erbil Polytechnic University, Department of Technical Information Systems Engineering, Erbil Technical Engineering College , Erbil-Iraq
• Lebanese French University, Department of Information Technology, College of Engineering and Computer Science, Erbil-Iraq

autor

Okur Mehmet Cudi

• Yasar University, Software Engineering Department, Faculty of Engineering, Izmir-Turkey

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