Machine Learning - Special issue on learning with probabilistic representations
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Approximate probability propagation with mixtures of truncated exponentials
International Journal of Approximate Reasoning
Extension of Bayesian Network Classifiers to Regression Problems
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Credible classification for environmental problems
Environmental Modelling & Software
Learning hybrid Bayesian networks using mixtures of truncated exponentials
International Journal of Approximate Reasoning
Feature selection for dimensionality reduction
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Environmental Modelling & Software
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
Good practice in Bayesian network modelling
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
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Bayesian networks are one of the most powerful tools in the design of expert systems located in an uncertainty framework. However, normally their application is determined by the discretization of the continuous variables. In this paper the naive Bayes (NB) and tree augmented naive Bayes (TAN) models are developed. They are based on Mixtures of Truncated Exponentials (MTE) designed to deal with discrete and continuous variables in the same network simultaneously without any restriction. The aim is to characterize the habitat of the spur-thighed tortoise (Testudo graeca graeca), using several continuous environmental variables, and one discrete (binary) variable representing the presence or absence of the tortoise. These models are compared with the full discrete models and the results show a better classification rate for the continuous one. Therefore, the application of continuous models instead of discrete ones avoids loss of statistical information due to the discretization. Moreover, the results of the TAN continuous model show a more spatially accurate distribution of the tortoise. The species is located in the Donana Natural Park, and in semiarid habitats. The proposed continuous models based on MTEs are valid for the study of species predictive distribution modelling.