Open AccessFalcon Optimization Algorithm for Bayesian Networks Structure Learning
Author(s) -
Shahab Wahhab Kareem,
Mehmet Cudi Okur
Publication year - 2021
Publication title -
computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.145
H-Index - 5
eISSN - 2300-7036
pISSN - 1508-2806
DOI - 10.7494/csci.2021.22.4.3773
Subject(s) - computer science , bayesian network , benchmark (surveying) , artificial intelligence , simulated annealing , machine learning , greedy algorithm , probabilistic logic , bayesian optimization , algorithm , geodesy , geography
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.