# A simple squared-error reformulation for ordinal classification

arXiv: https://arxiv.org/abs/1612.00775 · HTML: https://mlsanity.com/paper/1612.00775/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2016.

## Built on by (most cited first, 2 of 2)

- [Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation](https://mlsanity.com/paper/2202.05167/) (2022)
- [Class Distance Weighted Cross Entropy Loss for Classification of Disease Severity](https://mlsanity.com/paper/2412.01246/) (2024)

## Abstract (arXiv)

In this paper, we explore ordinal classification (in the context of deep neural networks) through a simple modification of the squared error loss which not only allows it to not only be sensitive to class ordering, but also allows the possibility of having a discrete probability distribution over the classes. Our formulation is based on the use of a softmax hidden layer, which has received relatively little attention in the literature. We empirically evaluate its performance on the Kaggle diabetic retinopathy dataset, an ordinal and high-resolution dataset and show that it outperforms all of the baselines employed.