Structure Learning of Bayesian Networks Using Elephant Swarm Water Search Algorithm

Abstract

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Bayesian networks can represent probabilistic dependency relationships among the variables. One strategy of Bayesian Networks structure learning is the score and search technique. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) as a novel approach to Bayesian network structure learning. In the algorithm, Deleting Reversing Inserting and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with simulated annealing and greedy search using BDe score function. The authors have also investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of the evaluations the proposed algorithm has better performance than the other algorithms and produces better scores and accuracy values.