FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
It addresses the need for scalable, efficient, and explainable models in classification tasks, particularly for mixed data, offering a solution that balances performance with interpretability.
The paper tackles the problem of multi-category classification with mixed data by introducing FOLD-RM, an inductive learning algorithm that generates explainable answer set programming rules, achieving competitive performance with state-of-the-art methods like XGBoost and MLPs, and outperforming XGBoost on some large datasets.
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.