A Selective Tree-Augmented Bayesian Network Classifier Based on Rough Set Theory
Fan Zhang · Fudan xuebao. Ziran Kexue ban · 2004
TAN is a state-of-the-art extension of naive Bayes that can express limited forms of inter-dependence among attributes. Rough sets theory provides tools for expressing inexact or partial dependencies within dataset. A variant of TAN using rough sets theory is presented,and their tree classifier structures, which can be thought of as a selective restricted trees Bayesian classifier, are compared. It delivers lower error than both pre-existing TAN-based classifiers, with substantially less computation than is required by the SuperParent approach.