Learning Right Sized Belief Networks by Means of a Hybrid Methodology

Authors:
Silvia Acid;Luis M. de Campos
Affiliations:
-;-
Venue:
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Year:
2000

Citing 6
Cited 1

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
an entropy-driven system for construction of probabilistic expert systems from databases

UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A hybrid anytime algorithm for the construction of causal models from sparse data

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
An algorithm for finding minimum d-separating sets in belief networks

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Review: learning bayesian networks: Approaches and issues

The Knowledge Engineering Review

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.