Abstract
In this paper we explore the extension of various Bayesian network classifiers to regression problems where some of the explanatory variables are continuous and some others are discrete. The goal is to compute the posterior distribution of the response variable given the observations, and then use that distribution to give a prediction. The involved distributions are represented as Mixtures of Truncated Exponentials. We test the performance of the proposed models on different datasets commonly used as benchmarks, showing a competitive performace with respect to the state-of-the-art methods.
This work has been supported by the Spanish Ministry of Education and Science, project TIN2007-67418-C03-02 and by Junta de Andalucía, project P05-TIC-00276.
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Fernández, A., Salmerón, A. (2008). Extension of Bayesian Network Classifiers to Regression Problems. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88309-8_9
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DOI: https://doi.org/10.1007/978-3-540-88309-8_9
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