Bayesian Classifier Structure-learning Using Several General Algorithms

The use of Bayesian Networks (BNs) as classifiers in different fields of application has recently witnessed a noticeable growth. Yet, the Naïve Bayes application, and even the augmented Naïve Bayes, to classifier-structure learning, has been vulnerable to certain limits, which explains the practitioners resort 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 present work's major objective lies in setting up a further solution whereby a remedy can be conceived for the intricate algorithmic complexity imposed during the learning of Bayesian classifiers structure with the use of sophisticated algorithms.