A new learning structure heuristic of bayesian networks from data

Authors:
Heni Bouhamed;Afif Masmoudi;Thierry Lecroq;Ahmed Rebaï
Affiliations:
LITIS EA 4108, University of Rouen, Mont-Saint-Aignan Cedex, France;Department of Mathematics, Faculty of Science of Sfax, Sfax University, Sfax, Tunisia;LITIS EA 4108, University of Rouen, Mont-Saint-Aignan Cedex, France;Bioinformatics Unit, Centre of Biotechnologie of Sfax, Sfax, Tunisia
Venue:
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2012

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Abstract

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 BN-structure 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 structure-learning 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.