A New Approach for Bayesian Classifier Learning Structure via K2 Algorithm

It is a well-known fact that the Bayesian Networks’ (BNs) use 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. Noteworthy, the present paper’s framework is organized as follows. We start, in the first place, by stating the BNs’ definition along with the problems related to their structure-learning from data. We, then, go on to propose a novel approach designed to reduce the algorithmic complexity without engendering any loss of information when learning the structure of a Bayesian classifier. Ultimately, our approach is to be tested on a car diagnosis, a Lymphography diagnosis and a cardiac disease diagnosis databases, along with our achieved results’ discussion, and an exposition of our conducted work’s interests as a closing step to this work.