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Classifier Pooling for Modern Ordinal Classification

arXiv cs.LG / 3/19/2026

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Key Points

  • The paper proposes a model-agnostic ordinal classification method that can adapt any non-ordinal classifier to handle ordinal labels.
  • It provides an open-source Python package implementing the algorithms, enabling easy adoption.
  • Empirical evaluation on multiple real-world datasets shows the method often outperforms standard non-ordinal approaches, especially with small sample sizes or many classes.
  • The work aims to broaden the use of modern ML for ordinal data by offering a practical, reusable software tool.

Abstract

Ordinal data is widely prevalent in clinical and other domains, yet there is a lack of both modern, machine-learning based methods and publicly available software to address it. In this paper, we present a model-agnostic method of ordinal classification, which can apply any non-ordinal classification method in an ordinal fashion. We also provide an open-source implementation of these algorithms, in the form of a Python package. We apply these models on multiple real-world datasets to show their performance across domains. We show that they often outperform non-ordinal classification methods, especially when the number of datapoints is relatively small or when there are many classes of outcomes. This work, including the developed software, facilitates the use of modern, more powerful machine learning algorithms to handle ordinal data.

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