Hybrid Bayesian network classifiers: Application to species distribution models

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.

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