Abstract
Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models tailored to solving multi-dimensional classification problems, where an instance has to be assigned to multiple class variables. In this paper, we propose a novel multi-dimensional classifier that consists of a classification tree with MBCs in the leaves. We present a wrapper approach for learning this classifier from data. An experimental study carried out on randomly generated synthetic data sets shows encouraging results in terms of predictive accuracy.
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Code available at https://github.com/ComputationalIntelligenceGroup/MBCTree.
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Acknowledgements
This work has been partially supported by the Spanish Ministry of Economy, Industry and Competitiveness through the Cajal Blue Brain (C080020-09; the Spanish partner of the Blue Brain initiative from EPFL) and TIN2016-79684-P projects, by the Regional Government of Madrid through the S2013/ICE-2845-CASI-CAM-CM project, and by Fundación BBVA grants to Scientific Research Teams in Big Data 2016.
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Gil-Begue, S., Larrañaga, P., Bielza, C. (2018). Multi-dimensional Bayesian Network Classifier Trees. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_38
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