Bayesian network structure learning based on pigeon inspired optimization

Abstract

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Probabilistic dependency relationships among the variables can be represented by Bayesian networks. One strategy of a structure learning Bayesian Networks is the score and search technique. In this paper present the proposed method for Bayesian network structure learning which is depended on Pigeon Inspired Optimization (PIO). The proposed method is a simple one among a firm concentration rate. In nature a navigational ability concerning pigeons is unbelievable and impressive. Under the PIO search algorithm we define a set of directed acyclic graphs. Every graph owns a score which shows its fitness. It iterates the algorithm until it gets the best solution or a satisfactory network structure using a landmark compass and map operator. During this work the proposed method compared with Simulated Annealing and Greedy Search using BDe score function. We also investigated the confusion matrix performances of the methods using various benchmark data sets. Specific effects show that a presented algorithm produces excellent performance than Simulated Annealing and Greedy algorithms and produces higher scores and accuracy values. © 2019 Elsevier B.V. All rights reserved.