Reducing the Structure Space of Bayesian Classifiers Using Some General Algorithms

The use of Bayesian Networks (BNs) as classifiers in different application fields has recently witnessed a noticeable growth. Yet, using the Naïve Bayes application, and even the augmented Naïve Bayes, to classifier‐structure learning, has been vulnerable to some extent, which accounts for the resort of experts to other more sophisticated types of algorithms. Consequently, the use of such algorithms has paved the way for raising the problem of super‐exponential increase in computational complexity of the Bayesian classifier learning structure, with the increasing number of descriptive variables. In this context, the main objective of our present work lies in trying to conceive further solutions to solve the problem of the intricate algorithmic complexity imposed during the learning of Bayesian classifiers structure through the use of sophisticated algorithms. Our results revealed that the newly suggested approach allows us to considerably reduce the execution time of the Bayesian classifier structure learning without any information loss.