Coherent Hierarchical Multi-Label Classification Networks
Published in
Neural Information Processing Systems(2020)External Links:
TL
TL;DR
This paper proposes C-HMCNN(h), a novel approach for HMC problems, which, given a network h for the underlying multi-label classification problem, exploits the hierarchy information in order to produce predictions coherent with the constraint and improve performance.
Abstract
Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes. In this paper, we propose C-HMCNN(h), a novel approach for HMC problems, which, given a network h for the underlying multi-label classification problem, exploits the hierarchy information in order to produce predictions coherent with the constraint and improve performance. We conduct an extensive experimental analysis showing the superior performance of C-HMCNN(h) when compared to state-of-the-art models.
References34 items
1
Machine learning for discovering missing or wrong protein function annotations
2
Hierarchical Classification Based on Label Distribution Learning
3
mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning
4
A hierarchical multi-label classification method based on neural networks for gene function prediction
5