BN-GEPSO: Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

Abstract

At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search for food). PSO is employed widely because it is easier to code, converges quickly, and can be parallelized easily. We use a recently proposed version of PSO called generalized particle swarm optimization (GEPSO) to learn bayesian network structure. We construct an initial directed acyclic graph (DAG) by using the max-min parent's children (MMPC) algorithm and cross relative average entropy. This DAG is used to create a population for the GEPSO optimization procedure. Moreover, we propose a velocity update procedure to increase the efficiency of the algorithmic search process. Results of the experiments show that as the complexity of the dataset increases, our algorithm Bayesian network generalized particle swarm optimization (BN-GEPSO) outperforms the PSO algorithm in terms of the Bayesian information criterion (BIC) score.