Using a Hierarchical Bayesian Model to Handle High Cardinality Attributes with Relevant Interactions in a Classification Problem

We employed a multilevel hierarchical Bayesian model in the task of exploiting relevant interactions among high cardinality attributes in a classification problem without overfitting. With this model, we calculate posterior class probabilities for a pattern W combining the observations of W in the training set with prior class probabilities that are obtained recursively from the observations of patterns that are strictly more generic than W . The model achieved performance improvements over standard Bayesian network methods like Naive Bayes and Tree Augmented Naive Bayes, over Bayesian Networks where traditional conditional probability tables were substituted byNoisy-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.