Global Hierarchical Neural Networks using Hierarchical Softmax
Jetze Schuurmans Flavius Frasincar
This paper presents a framework in which hierarchical softmax is used to
create a global hierarchical classifier. The approach is applicable for any
classification task where there is a natural hierarchy among classes. We show
empirical results on four text classification datasets. In all datasets the
hierarchical softmax improved on the regular softmax used in a flat classifier
in terms of macro-F1 and macro-recall. In three out of four datasets
hierarchical softmax achieved a higher micro-accuracy and macro-precision.