A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks

TL;DR

This paper introduces a top-down supervised learning model for hierarchical multi-label classification in networks, demonstrating its effectiveness and scalability through a gene function prediction case study.

Contribution

It presents a novel top-down classification approach for hierarchical multi-label classification that improves prediction efficiency and scalability compared to existing probabilistic models.

Findings

Achieves high prediction accuracy in gene function classification

Scales better than probabilistic models in large networks

Maintains competitive computational costs

Abstract

Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification (HMC), where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica, a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. The results in this work support the working hypothesis that the proposed model can achieve good levels of prediction efficiency, while scaling up in relation to the state of the art.