HPB: A Model for Handling BN Nodes with High Cardinality Parents
Jorge Jambeiro Filho, Jacques Wainer; 9(70):2141−2170, 2008.
Abstract
We replaced the conditional probability tables of Bayesian network nodes whose parents have high cardinality with a multilevel empirical hierarchical Bayesian model called hierarchical pattern Bayes (HPB). The resulting Bayesian networks achieved significant performance improvements over Bayesian networks with the same structure and traditional conditional probability tables, over Bayesian networks with simpler structures like naïve Bayes and tree augmented naïve Bayes, over Bayesian networks where traditional conditional probability tables were substituted by noisy-OR gates, default tables, decision trees and decision graphs and over Bayesian networks constructed after a cardinality reduction preprocessing phase using the agglomerative information bottleneck method. Our main tests took place in important fraud detection domains, which are characterized by the presence of high cardinality attributes and by the existence of relevant interactions among them. Other tests, over UCI data sets, show that HPB may have a quite wide applicability.
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