Description
Title: Bayesian network structure learning using the Falcon optimization algorithm
Abstract: Bayesian networks are one of the useful scientific models in machine learning when creating a structure of knowledge. They are able to depict the probabilistic dependencies between numerous variables. A technique for learning the structure of a Bayesian network is the score and search method. The learning structure of a Bayesian network is subjected to the authors’ application of the falcon optimization algorithm (FOA). In order to obtain the FOA for approaching the ideal solution of a structure, the following operations were used in this paper: reversing, deleting, moving, and inserting. The FOA algorithm essentially employs the falcon prey search strategy. The outcome of the suggested method combines the BDeu score function with greedy search, simulated annealing, and optimization inspired by pigeons. The performance of the confusion matrix for these techniques was also examined by the authors using various benchmark data sets. The experimental results demonstrate that the proposed method performs more consistently than other algorithms (including the production of excellent scores and accuracy values).
Keywords: Bayesian network, global search, falcon optimization algorithm, structure learning, search and score
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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