A New general learning structure approach of Bayesian networks from data

Nowadays, Bayesian Networks (BNs) have constituted one of the most complete, self-sustained and coherent formalisms useful for knowledge acquisition, representation and application through computer systems. Yet, the learning of these BNs structures from data represents a problem classified at an NP-hard range of difficulty. As such, it has turned out to be the most exciting challenge in the learning machine area. In this context, the present work’s major objective lies in setting up a further solution conceived to be a remedy for the intricate algorithmic complexity problems imposed during the learning of BNstructure through a massively-huge data backlog. Our present work has been constructed according to the following framework; on a first place, we are going to proceed by defining BNs and their related problems of structurelearning from data. We, then, go on to propose a novel heuristic designed to reduce the algorithmic complexity without engendering any loss of information. Ultimately, our conceived approach will be tested on a car diagnosis as well as on a Lymphography diagnosis data-bases, while our achieved results would be discussed, along with an exposition of our conducted work’s interests as a closing step to this work.