Classification Using a Bayesian Network in SAS® Enterprise Minert

• Activity Number: 640
• Type: Topic Contributed
• Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Computing
• Author(s): Yongqiao Xiao*+ and Taiyeong Lee and Jared Dean
• Companies: SAS Institute and SAS Institute and SAS Institute
• Keywords: Bayesian Networks ; Supervised Learning ; Markov Blanket

Abstract

A Bayesian network is a directed acyclic graphical model in which nodes represent random variables and the arcs between nodes represent conditional dependency of the random variables. Because the Bayesian network provides conditional independence structure and a conditional probability table at each node, the model has been used successfully as a predictive model in supervised data mining. Using a newly developed high-performance Bayesian network procedure (PROC HPBNET), SAS Enterprise Miner implements the model primarily as a classification tool, which includes naïve Bayes, tree-augmented naïve Bayes, Bayesian network-augmented naïve Bayes, parent child Bayesian network, and Markov blanket Bayesian network classifiers. Examples that use real data sets illustrate the performance of the implemented Bayesian network classifiers.