Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting

Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting (1998)

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by Nir Friedman , Moises Goldszmidt , Thomas J. Lee

Venue:	In Proceedings of the International Conference on Machine Learning (ICML
Citations:	23 - 2 self

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Datum	Value	Source
TITLE	Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting	user correction - Legacy Corrections
AUTHOR NAME	Nir Friedman	SVM HeaderParse 0.1
AUTHOR AFFIL	Computer Science Division; University of California	SVM HeaderParse 0.2
AUTHOR ADDR	Berkeley, CA 94920	SVM HeaderParse 0.1
AUTHOR NAME	Moises Goldszmidt	SVM HeaderParse 0.1
AUTHOR AFFIL	SRI International	SVM HeaderParse 0.2
AUTHOR ADDR	333 Ravenswood Avenue; Menlo Park, CA 94025	SVM HeaderParse 0.1
AUTHOR NAME	Thomas J. Lee	SVM HeaderParse 0.1
AUTHOR AFFIL	SRI International	SVM HeaderParse 0.2
AUTHOR ADDR	333 Ravenswood Avenue; Menlo Park, CA 94025	SVM HeaderParse 0.1
ABSTRACT	In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs competitively with C4.5 and other state-of-the-art methods. This classifier has several advantages including robustness and polynomial computational complexity. One limitation of the TAN classifier is that it applies only to discrete attributes, and thus, continuous attributes must be prediscretized. In this paper, we extend TAN to deal with continuous attributes directly via parametric (e.g., Gaussians) and semiparametric (e.g., mixture of Gaussians) conditional probabilities. The result is a classifier that can represent and combine both discrete and continuous attributes. In addition, we propose a new method that takes advantage of the modeling language of Bayesian networks in order to represent attributes both in discrete and continuous form simultaneously, and use both versions in the classifi...	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	In Proceedings of the International Conference on Machine Learning (ICML	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	179--187	INFERENCE
CITATIONS	17 found	ParsCit 1.0