Phylo-HS: A phylogenetic hierarchical softmax for taxonomic classification

Abstract

Taxonomic binning -assigning taxonomic labels to DNA sequencing reads - is a core component of metagenomics data analysis. While machine learning approaches offer competitive speed and accuracy, their scalability is hindered by the growing number of referenced genomes and species. A major bottleneck lies in the final softmax layer of neural network models, which computes probabilities and gradients for all outcome classes. To address this, we propose Phylo-HS, a hierarchical softmax method that leverages the taxonomic tree to group classes into meaningful clusters. Phylo-HS achieves an order of magnitude speed improvement on a dataset with 5,000 classes and improves classification accuracy compared to frequency-based hierarchical softmax methods. By integrating phylogenetic structure into the model, Phylo-HS effectively balances scalability and accuracy for large-scale metagenomic analysis.