Learning Right Sized Belief Networks by Means of a Hybrid Methodology

Previous algoritms for the construction of belief networks structures from data are mainly based either on independence criteria or on scoring metrics. The aim of this paper is to present a hybrid methodology that is a combination of these two approaches, which benefits from characteristics of each one, and to introduce an operative algoritm based on this methodology. We dedicate a special attention to the problem of getting the `right' size of the belief network induced from data, i.e. finding a trade-off between network complexity and accuracy. We propose several approaches to tackle this matter. Results of the evaluation of the algorithm on the well-known Alarm network are also presented.

• Type: Conference
• Year: 2000
• Where: PKDD
• Authors: Silvia Acid, Luis M. de Campos
• Keywords: Belief Network, Belief Networks Structures, Data Mining, Hybrid Methodology, PKDD 2000