Noncrossing Ordinal Classification

TL;DR

This paper introduces a noncrossing ordinal classification method that improves the accuracy of classifying ordinal data by preventing boundary crossing ambiguities through noncrossing constraints, supported by theoretical and empirical evidence.

Contribution

It proposes a novel noncrossing ordinal classification framework that enforces constraints to enhance classification accuracy for ordinal data.

Findings

Improved classification performance on simulated data

Effective in real data applications

Reduces ambiguity caused by boundary crossing

Abstract

Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We consider a framework of ordinal classification which pools the results from binary classifiers together. An inherent difficulty of this framework is that the class prediction can be ambiguous due to boundary crossing. To fix this issue, we propose a noncrossing ordinal classification method which materializes the framework by imposing noncrossing constraints. An asymptotic study of the proposed method is conducted. We show by simulated and data examples that the proposed method can improve the classification performance for ordinal data without the ambiguity caused by boundary crossings.