Building Classifiers using Bayesian Networks

Building Classifiers using Bayesian Networks (1996)

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by Nir Friedman , Moises Goldszmidt

Venue:	In Proceedings of the thirteenth national conference on artificial intelligence
Citations:	72 - 2 self

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Datum	Value	Source
TITLE	Building Classifiers using Bayesian Networks	user correction - Legacy Corrections
AUTHOR NAME	Nir Friedman	SVM HeaderParse 0.1
AUTHOR AFFIL	Stanford University; Dept. of Computer Science	SVM HeaderParse 0.2
AUTHOR ADDR	Gates Building 1A; Stanford, CA 94305-9010	SVM HeaderParse 0.1
AUTHOR NAME	Moises Goldszmidt	SVM HeaderParse 0.1
AUTHOR AFFIL	Rockwell Science Center	SVM HeaderParse 0.2
AUTHOR ADDR	444 High St., Suite 400; Palo Alto, CA 94301	SVM HeaderParse 0.1
ABSTRACT	Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we examine and evaluate approaches for inducing classifiers from data, based on recent results in the theory of learning Bayesian networks. Bayesian networks are factored representations of probability distributions that generalize the naive Bayes classifier and explicitly represent statements about independence. Among these approacheswe single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness which are characteristic of naive Bayes. We experimentally tested these approaches using benchmark problems from...	user correction - Legacy Corrections
YEAR	1996	INFERENCE
VENUE	In Proceedings of the thirteenth national conference on artificial intelligence	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	1277--1284	INFERENCE
CITATIONS	18 found	ParsCit 1.0