Classifier Pooling for Modern Ordinal Classification

2026-03-17

AI Summary

This work addresses the lack of general-purpose methods and open-source tools for ordinal classification by proposing a model-agnostic framework that transforms any base classifier into an ordinal-aware variant. The approach integrates a classifier pooling strategy with ordinal constraint mechanisms, enabling, for the first time, universal adaptation of arbitrary classifiers to ordinal data. To support reproducibility and practical adoption, the authors release an open-source Python package that fills a critical gap in available ordinal classification tooling. Extensive experiments on multiple real-world datasets demonstrate that the proposed method significantly outperforms conventional non-ordinal classifiers, particularly in small-sample and high-cardinality settings, thereby confirming its effectiveness and practical utility.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• ordinal classification
• machine learning
• classifier pooling
• ordered data
• model-agnostic

Innovation

• ordinal classification
• model-agnostic
• classifier pooling
• machine learning
• open-source software