# A neural network approach to ordinal regression

## TL;DR

This paper introduces NNRank, a neural network-based method for ordinal regression that outperforms some existing methods and offers advantages like scalability and rapid prediction, suitable for large-scale data tasks.

## Contribution

The paper presents NNRank, a neural network approach for ordinal regression that generalizes perceptron methods and performs competitively with Gaussian process and SVM-based methods.

## Key findings

- NNRank outperforms neural network classification on benchmark datasets.
- NNRank achieves comparable performance to Gaussian process and SVM ordinal regression methods.
- NNRank supports online and batch learning, handling large datasets efficiently.

## Abstract

Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe a simple and effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data processing tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics.