# A Novel Large-scale Ordinal Regression Model

## TL;DR

This paper introduces an efficient linear NPSVOR model for large-scale ordinal regression, utilizing dual coordinate descent for faster training and a new prediction function to leverage label order, demonstrated on text datasets.

## Contribution

It develops a scalable training method for linear NPSVOR using DCD and proposes a new prediction function that exploits label order information.

## Key findings

- DCD significantly speeds up training on large datasets.
- The proposed model achieves competitive performance on text OR tasks.
- Efficient training makes large-scale ordinal regression more practical.

## Abstract

Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels. Recent years, people share their opinions and sentimental judgments conveniently with social networks and E-Commerce so that plentiful large-scale OR problems arise. However, few studies have focused on this kind of problems. Nonparallel Support Vector Ordinal Regression (NPSVOR) is a SVM-based OR model, which learns a hyperplane for each rank by solving a series of independent sub-optimization problems and then ensembles those learned hyperplanes to predict. The previous studies are focused on its nonlinear case and got a competitive testing performance, but its training is time consuming, particularly for large-scale data. In this paper, we consider NPSVOR's linear case and design an efficient training method based on the dual coordinate descent method (DCD). To utilize the order information among labels in prediction, a new prediction function is also proposed. Extensive contrast experiments on the text OR datasets indicate that the carefully implemented DCD is very suitable for training large data.