A kernel-based semi-naive Bayesian classifier using P-Trees

A novel semi-naive Bayesian classifier is introduced that is particularly suitable to data with many attributes. The naive Bayesian classifier is taken as a starting point and correlations are reduced through joining of highly correlated attributes. Our technique differs from related work in its use of kernel-functions that systematically include continuous attributes rather than relying on discretization as a preprocessing step. This retains distance information within the attribute domains and ensures that attributes are joined based on their correlation for the particular values of the test sample. We implement a kernel-based semi-naive Bayesian classifier using P-Trees and demonstrate that it generally outperforms the naive Bayesian classifier as well as a discrete semi-naive Bayesian classifier.