  The Tree Augmented Naive Bayes (TAN) classifier is a type of probabilistic
graphical model that constructs a single-parent dependency tree to estimate the
distribution of the data. In this work, we propose two novel Hierarchical
dependency-based Tree Augmented Naive Bayes algorithms, i.e. Hie-TAN and
Hie-TAN-Lite. Both methods exploit the pre-defined parent-child
(generalisation-specialisation) relationships between features as a type of
constraint to learn the tree representation of dependencies among features,
whilst the latter further eliminates the hierarchical redundancy during the
classifier learning stage. The experimental results showed that Hie-TAN
successfully obtained better predictive performance than several other
hierarchical dependency constrained classification algorithms, and its
predictive performance was further improved by eliminating the hierarchical
redundancy, as suggested by the higher accuracy obtained by Hie-TAN-Lite.
