FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely-used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons (MLPs), however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.
@article{arxiv.2202.06913,
title = {FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data},
author = {Huaduo Wang and Farhad Shakerin and Gopal Gupta},
journal= {arXiv preprint arXiv:2202.06913},
year = {2022}
}
Comments
Paper presented at the 38th International Conference on Logic Programming (ICLP 2022), 16 pages