Learning Naive Bayes Classifiers From Attribute Value Taxonomies and Partially Specified Data
Jun Zhang, Vasant Honavar · Iowa State University Digital Repository (Iowa State University) · 2004
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- an extension of the Naïve Bayes Learning algorithm that effectively exploits user-supplied attribute value taxonomies to construct compact and accurate Naïve Bayes classifiers from partially specified data. Our experiments with benchmark data sets and AVTs show that AVT-NBL yields classifiers that are substantially more accurate and more compact than those obtained using the standard Naïve Bayes learner. I.