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Multi-dimensional Bayesian Network Classifier Trees

  • Santiago Gil-Begue
  • Pedro Larrañaga
  • Concha Bielza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11314)

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.

Keywords

Multi-dimensional and multi-label supervised classification problems Bayesian networks Classification trees Meta-classifiers Hybrid classifiers Performance evaluation measures 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Santiago Gil-Begue
    • 1
  • Pedro Larrañaga
    • 1
  • Concha Bielza
    • 1
  1. 1.Universidad Politécnica de MadridBoadilla del MonteSpain

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