Structure space of Bayesian networks is dramatically reduced by subdividing it in sub-networks

Heni Bouhamed ^[2] ; Afif Masmoudi ^[2] ; Thierry Lecroq ^[1] ; Ahmed Rebaï ^[2]
1. [1] University of Rouen
  
  University of Rouen
  
  Arrondissement de Rouen, Francia
2. [2] Sfax University, Tunisia
Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 287, Nº 1 (15 October 2015), 2015, págs. 48-62
Idioma: inglés
DOI: 10.1016/j.cam.2015.02.055
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Resumen
- Currently, Bayesian Networks (BNs) have become one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems. However, learning of BNs structures from data has been shown to be an NP-hard problem. It has turned out to be one of the most exciting challenges in machine learning. 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 imposed during the learning of BN-structure with a massively-huge data backlog.