Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Analysis in HUGIN of data conflict
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
On-line viscosity virtual sensor for optimizing the combustion in power plants
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Viscosity virtual sensor to control combustion in fossil fuel power plants
Engineering Applications of Artificial Intelligence
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Several learning algorithms have been proposed to construct probabilistic models from data using the Bayesian networks mechanism. Some of them permit the participation of human experts in order to create a knowledge representation of the domain. However, multiple different models may result for the same problem using the same data set. This paper presents the experiences in the construction of a probabilistic model that conforms a viscosity virtual sensor. Several experiments have been conduced and several different models have been obtained. This paper describes the evaluation implemented of all models under different criteria. The analysis of the models and the conclusions identified are included in this paper.