Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior

TL;DR

This paper presents a Bayesian hierarchy-based prior for multinomial logit models to improve classification accuracy when class hierarchies are known, especially with limited training data.

Contribution

Introduces a novel hierarchy-based Bayesian prior for multinomial logit models, enhancing classification performance using class hierarchy information.

Findings

The new method outperforms standard MNL models on simulated data.

It performs better on document labeling tasks with small training datasets.

Hierarchy-based prior improves classification accuracy in hierarchical class settings.

Abstract

We introduce a new method for building classification models when we have prior knowledge of how the classes can be arranged in a hierarchy, based on how easily they can be distinguished. The new method uses a Bayesian form of the multinomial logit (MNL, a.k.a. ``softmax'') model, with a prior that introduces correlations between the parameters for classes that are nearby in the tree. We compare the performance on simulated data of the new method, the ordinary MNL model, and a model that uses the hierarchy in different way. We also test the new method on a document labelling problem, and find that it performs better than the other methods, particularly when the amount of training data is small.