NDLI: Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers

Content Provider	IEEE Xplore Digital Library
Author	Flores, M.J. Gamez, J.A. Martinez, A.M. Salmeron, A.
Copyright Year	2011
Description	Author affiliation: Computer Systems Department, SIMD, I3A., University of Castilla-La Mancha, Albacete, Spain (Flores, M.J.; Gamez, J.A.; Martinez, A.M.) \|\| Department of Statistics and Applied Mathematics, University of Almería, Almería, Spain (Salmeron, A.)
Abstract	The Averaged One-Dependence Estimators (AODE) classifier is one of the most attractive semi-naive Bayesian classifiers and hence a good alternative to Naive Bayes (NB), as it obtains fairly low error rates maintaining under control the computational complexity. Unfortunately, as most of the methods designed within the framework of Bayesian networks, AODE is exclusively defined to deal with discrete variables. Several approaches to avoid the use of discretization pre-processing techniques have already been presented, all of them involving in lower or greater degree the assumption of (conditional) Gaussian distributions. In this paper, we propose the use of Mixture of Truncated Exponentials (MTEs), whose expressive power to accurately approximate the most commonly used distributions for hybrid networks has already been demonstrated. We perform experiments on the use of MTEs over a large group of datasets for the first time, and we analyze the importance of selecting a proper number of points when learning MTEs for NB and AODE, as we believe, it is decisive to provide accurate results.
Starting Page	593
Ending Page	598
File Size	271978
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121720
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training AODE Accuracy Bayesian methods mixtures of truncated exponentials Estimation Numerical models Complexity theory continous variables Niobium
Content Type	Text
Resource Type	Article

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