An improved evolutionary approach-based hybrid algorithm for Bayesian network structure learning in dynamic constrained search space

Abstract

Learning Bayesian network (BN) structures from data is a NP-hard problem due to the vastness of the solution space. To address this issue, hybrid approaches that integrate the constraint-based (CB) method and the score-and-search (SS) method have been developed in the literature, but when the constrained search space is fixed and inaccurate, it is very likely to lose the optimal solution, leading to low learning accuracy. Besides, due to the randomness and uncertainty of the search, it is difficult to preserve the superiority of the structures, resulting in low learning efficiency. Therefore, we propose a novel hybrid algorithm based on an improved evolutionary approach to explore BN

Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

“…As a typical representative of the probability graph model, the Bayesian Network (BN) is an essential theoretical tool to represent uncertain knowledge and inference [1,2]. BN is a crucial branch of machine learning, compared with other artificial intelligence algorithms [3], BN has good interpretability.…”