Learning the structure of large-scale bayesian networks using genetic algorithm
Fatemeh Vafaee · 2014
Bayesian networks are probabilistic graphical models representing conditional dependencies among a set of random variables. Due to their concise representation of the joint probability distribution, Bayesian Networks are becoming incrementally popular models for knowledge representation and reasoning in various problem domains. However, learning the structure of the Bayesian networks is an NP-hard problem since the number of structures grows super-exponentially as the number of variables increases. This work therefore is aimed to propose a new hybrid structure learning algorithm that uses mutual dependencies to reduce the search space complexity and recruits the genetic algorithm to effectively search over the reduced space of possible structures. The proposed method is best suited for problems with medium to large number of variables and a limited dataset. It is shown that the proposed method achieves higher model's accuracy as compared to a series of popular structure learning algorithms particularly when the data size gets smaller.