Learning Bayesian Classifiers from Dependency Network Classifiers

Authors:
José A. Gámez;Juan L. Mateo;José M. Puerta
Affiliations:
Computing Systems Department, Intelligent Systems and Data Mining Group --- i3A, University of Castilla-La Mancha, Albacete, 02071, Spain;Computing Systems Department, Intelligent Systems and Data Mining Group --- i3A, University of Castilla-La Mancha, Albacete, 02071, Spain;Computing Systems Department, Intelligent Systems and Data Mining Group --- i3A, University of Castilla-La Mancha, Albacete, 02071, Spain
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Year:
2007

Citing 3
Cited 0

Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Dependency networks for inference, collaborative filtering, and data visualization

The Journal of Machine Learning Research

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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.