Search papers, labs, and topics across Lattice.
This paper introduces a model-agnostic ordinal classification method called Classifier Pooling, which leverages standard non-ordinal classifiers for ordinal tasks. The approach is implemented as an open-source Python package and evaluated on real-world datasets. Results demonstrate that Classifier Pooling often outperforms non-ordinal methods, particularly in data-scarce or high-cardinality scenarios, making modern machine learning more accessible for ordinal data analysis.
Unlock the power of your favorite classifier for ordinal data: Classifier Pooling consistently beats standard methods, especially when data is scarce or categories are numerous.
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.
