# Cumulative link models for deep ordinal classification

## TL;DR

This paper introduces a deep neural network approach for ordinal classification using cumulative link models, integrating probabilistic link functions and a distance-aware loss function, demonstrating improved performance over existing methods.

## Contribution

It presents a novel deep ordinal regression model based on cumulative link functions combined with a weighted Kappa loss, enhancing classification accuracy for ordinal data.

## Key findings

- Models outperform nominal classifiers on ordinal tasks
- Incorporating distance-based loss improves results
- Statistical tests confirm model superiority

## Abstract

This paper proposes a deep convolutional neural network model for ordinal regression by considering a family of probabilistic ordinal link functions in the output layer. The link functions are those used for cumulative link models, which are traditional statistical linear models based on projecting each pattern into a 1-dimensional space. A set of ordered thresholds splits this space into the different classes of the problem. In our case, the projections are estimated by a non-linear deep neural network. To further improve the results, we combine these ordinal models with a loss function that takes into account the distance between the categories, based on the weighted Kappa index. Three different link functions are studied in the experimental study, and the results are contrasted with statistical analysis. The experiments run over two different ordinal classification problems and the statistical tests confirm that these models improve the results of a nominal model and outperform other robust proposals considered in the literature.