Abstract
In this paper we analyse the use of probabilistic decision graphs in supervised classification problems. We enhance existing models with the ability of operating in hybrid domains, where discrete and continuous variables coexist. Our proposal is based in the use of mixtures of truncated basis functions. We first introduce a new type of probabilistic graphical model, namely probabilistic decision graphs with mixture of truncated basis functions distribution, and then present an initial experimental evaluation where our proposal is compared with state-of-the-art Bayesian classifiers, showing a promising behaviour.
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Alcalá-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J. Mult.-Valued Log. Soft Comput. 17, 255–287 (2011)
Bozga, M., Maler, O.: On the Representation of Probabilities over Structured Domains. In: Halbwachs, N., Peled, D.A. (eds.) CAV 1999. LNCS, vol. 1633, pp. 261–273. Springer, Heidelberg (1999)
Cobb, B.R., Shenoy, P.P.: Inference in Hybrid Bayesian Networks with Mixtures of Truncated Exponentials. Int. J. Approximate Reasoning 41, 257–286 (2006)
Flores, M.J., Gámez, J.A., Nielsen, J.D.: The PDG-mixture Model for Clustering. In: 11th Int. Conf. on Data Warehousing & Knowledge Discovery, pp. 378–389 (2009)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers. Machine Learning 29, 131–163 (1997)
Gámez, J.A., Nielsen, J.D., Salmerón, A.: Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs. Int. J. Approximate Reasoning 53, 929–945 (2012)
Horthorn, T., Hornik, K., van de Wiel, M.A., Zeileis, A.: Implementing a Class of Permutation Tests: The coin Package. J. Stat. Soft. 28, 1–23 (2008)
Jaeger, M.: Probabilistic Decision Graphs - Combining Verification and AI Techniques for Probabilistic Inference. Int. J. Uncertainty Fuzziness Knowledge Based Syst. 12, 19–42 (2004)
Langseth, H., Nielsen, T.D., Rumí, R., Salmerón, A.: Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials. Int. J. Approximate Reasoning 51, 485–498 (2010)
Langseth, H., Nielsen, T.D., Rumí, R., Salmerón, A.: Mixtures of Truncated Basis Functions. Int. J. Approximate Reasoning 53, 212–227 (2012)
Langseth, H., Nielsen, T.D., Pérez-Bernabé, I., Salmerón, A.: Learning mixtures of truncated basis functions from data. Int. J. Approximate Reasoning 55, 940–956 (2014)
Lauritzen, S.L., Wermuth, N.: Graphical Models For Associations Between Variables, Some of Which Are Qualitative and Some Quantitative. The Annals of Statistics 17, 31–57 (1989)
López-Cruz, P.L., Bielza, C., Larrañaga, P.: Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. Int. J. Approximate Reasoning 55, 989–1010 (2014)
Moral, S., Rumí, R., Salmerón, A.: Mixtures of Truncated Exponentials in Hybrid Bayesian Networks. In: Benferhat, S., Besnard, P. (eds.) ECSQARU 2001. LNCS (LNAI), vol. 2143, pp. 135–143. Springer, Heidelberg (2001)
Nielsen, J.D., Jaeger, M.: An Empirical Study of Efficiency and Accuracy of Probabilistic Graphical Models. In: Third European Workshop on Probabilistic Graphical Models, pp. 215–222 (2006)
Nielsen, J.D., Rumí, R., Salmerón, A.: Supervised Classification Using Probabilistic Decision Graphs. Comput. Stat. Data Anal. 53, 1299–1311 (2009)
Romero, V., Rumí, R., Salmerón, A.: Learning Hybrid Bayesian Networks Using Mixtures of Truncated Exponentials. Int. J. Approximate Reasoning 42, 54–68 (2006)
Shenoy, P., West, J.: Inference in Hybrid Bayesian Networks Using Mixtures of Polynomials. Int. J. Approximate Reasoning 52, 641–657 (2011)
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Fernández, A., Rumí, R., del Sagrado, J., Salmerón, A. (2014). Supervised Classification Using Hybrid Probabilistic Decision Graphs. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_14
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DOI: https://doi.org/10.1007/978-3-319-11433-0_14
Publisher Name: Springer, Cham
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