Using Ontologies To Improve Performance In Massively Multi-label Prediction

Ethan Steinberg, Peter J. Liu

Abstract: Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels. We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.

Keywords: multi-label, Bayesian network, ontology

TL;DR: We propose a new method for using ontology information to improve performance on massively multi-label prediction/classification problems.