archives

Learning Na´ve Bayes Classifiers From Attribute Value Taxonomies and Partially Specified Data


Home 

About 

Browse 

Search 

Register 

Subscriptions 

Deposit Papers 

Help
    

Zhang, Jun and Honavar, Vasant (2004) Learning Na´ve Bayes Classifiers From Attribute Value Taxonomies and Partially Specified Data. Technical Report TR 04-03, Department of Computer Science, Iowa State University.

Full text available as:Adobe PDF

Abstract

Partially specified data are commonplace in many practical applications of machine learning where different instances are described at different levels of precision relative to an attribute value taxonomy (AVT). This paper describes AVT-NBL -- a variant of the Na´ve Bayes Learning algorithm that effectively exploits user-supplied attribute value taxonomies to construct compact and accurate Na´ve Bayes classifiers from partially specified data. Our experiments with several data sets and AVTs show that AVT-NBL yields classifiers that are substantially more accurate and more compact than those obtained using the standard Na´ve Bayes learner.

Subjects:Computing Methodologies: ARTIFICIAL INTELLIGENCE: Learning (K.3.2)
ID code:00000335
Deposited by:Jun Zhang on 03 June 2004



Contact site administrator at: ssg@cs.iastate.edu