GAODE and HAODE: two proposals based on AODE to deal with continuous variables

Authors:
M. Julia Flores;José A. Gámez;Ana M. Martínez;José M. Puerta
Affiliations:
Intelligent Systems and Data Mining group, Albacete, Spain;Intelligent Systems and Data Mining group, Albacete, Spain;Intelligent Systems and Data Mining group, Albacete, Spain;Intelligent Systems and Data Mining group, Albacete, Spain
Venue:
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Year:
2009

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Abstract

AODE (Aggregating One-Dependence Estimators) is considered one of the most interesting representatives of the Bayesian classifiers, taking into account not only the low error rate it provides but also its efficiency. Until now, all the attributes in a dataset have had to be nominal to build an AODE classifier or they have had to be previously discretized. In this paper, we propose two different approaches in order to deal directly with numeric attributes. One of them uses conditional Gaussian networks to model a dataset exclusively with numeric attributes; and the other one keeps the superparent on each model discrete and uses univariate Gaussians to estimate the probabilities for the numeric attributes and multinomial distributions for the categorical ones, it also being able to model hybrid datasets. Both of them obtain competitive results compared to AODE, the latter in particular being a very attractive alternative to AODE in numeric datasets.