Abstract
In this paper we propose a new method for learning Bayesian network classifiers in an indirect way instead of directly from data. This new model is a classifier based on dependency networks [1] that is a probabilistic graphical model similar to Bayesian networks but in which directed cycles are allowed. The benefits from doing things in this way are that learning process for dependency networks can be easier and simpler than learning Bayesian networks, with the direct consequence that learning algorithms could have good properties about scalability. We show that it is possible to take advantage of this facility to get Bayesian networks classifiers without losing quality in classification.
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© 2007 Springer Berlin Heidelberg
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Gámez, J.A., Mateo, J.L., Puerta, J.M. (2007). Learning Bayesian Classifiers from Dependency Network Classifiers. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_90
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DOI: https://doi.org/10.1007/978-3-540-71618-1_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
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