Falcon Optimization Algorithm for Bayesian Networks Structure Learning





Bayesian Network, Global Search, Falcon Optimization algorithm, Structure Learning, Search and Score.


In machine-learning, one of the useful scientific models for producing the structure of knowledge is Bayesian network, which can draw probabilistic dependency relationships between variables. The score and search is a method used for learning the structure of a Bayesian network. The authors apply the Falcon Optimization Algorithm (FOA) as a new approach to learning the structure of Bayesian networks. This paper uses the Reversing, Deleting, Moving and Inserting operations to adopt the FOA for approaching the optimal solution of Bayesian network structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is compared with Pigeon Inspired optimization, Greedy Search, and Simulated Annealing using the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques utilizing several benchmark data sets. As shown by the evaluations, the proposed method has more reliable performance than the other algorithms including producing better scores and accuracy values.


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Author Biography

Shahab Wahhab Kareem, Erbil Polytechnic University

Receive a BSc in Control and Computer Engineering from the University of Technology Baghdad in 2001. In 2002 He was hired as Engineer Computer System Department in Technical Institute. In 2009 He Received His MSc in Software Engineering Department from Salahadeen University. He's a Ph.D. in the Computer Engineering Department, Faculty of Engineering from Yaşar University, Izmir Turkey 2020. He starts lecturer at Information System Engineering, Erbil technical Engineering College, Erbil Polytechnic University at 2011 till now.


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How to Cite

Kareem, S. W., & Okur, M. C. (2021). Falcon Optimization Algorithm for Bayesian Networks Structure Learning. Computer Science, 22(4). https://doi.org/10.7494/csci.2021.22.4.3773