Global Hierarchical Neural Networks using Hierarchical Softmax
Machine Learning
2023-08-03 v1 Computation and Language
Machine Learning
Abstract
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.
Cite
@article{arxiv.2308.01210,
title = {Global Hierarchical Neural Networks using Hierarchical Softmax},
author = {Jetze Schuurmans and Flavius Frasincar},
journal= {arXiv preprint arXiv:2308.01210},
year = {2023}
}
Comments
Submitted to the 35th Symposium on Applied Computing (SAC'20, https://www.sigapp.org/sac/sac2020/), to the Machine Learning and its Applications track (MLA, https://sites.google.com/view/acmsac2020/)