Year: 2025
Type: book-chapter
Abstract: We propose XGBOrdinal, an extension of XGBoost designed for ordinal classification problems commonly found in fields like medicine, where outcomes are often represented as scores, scales, stages, or grades. The proposed approach builds on the theoretical method introduced by Frank and Hall (2001) to transform an ordinal classification problem into a series of binary classification problems. Evaluated on multiple datasets, XGBOrdinal outperformed XGBClassifier and XGBRegressor, as well as existing ordinal methods. The implementation is fully compatible with GridSearchCV and RandomizedSearchCV, making it a scalable and efficient solution for handling ordinal data in machine learning pipelines. The used code is available open source (https://github.com/digital-medicine/XGBOrdinal).
DOI: 10.3233/shti250380
Authors: Fabian Kahl, Iris Kahl, Stephan M. Jonas
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410448451