将普通分类模型改造为有序分类，提升小样本和多类别场景表现
Classifier Pooling for Modern Ordinal Classification
- 通用方法：可兼容任意非有序分类器进行有序建模
- 实测优于传统方法，尤其在样本少或类别多时提升明显
- 开源工具包支持落地，助力医疗等领域的有序数据处理
有序数据广泛存在于临床及其他领域，但缺乏现代机器学习方法和公开可用的软件工具。本文提出一种模型无关的有序分类方法，可将任意非有序分类方法以有序方式应用。同时提供开源的Python实现。在多个真实数据集上验证了该方法在跨领域任务中的性能表现。结果表明，在样本量较小或输出类别较多时，该方法通常优于非有序分类方法。本工作及其开发的软件，推动了更强大现代机器学习算法在有序数据处理中的应用。
原文摘要 · Abstract (English)
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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