FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data

TL;DR

FOLD-RM is an automated, scalable, and explainable inductive learning algorithm that generates interpretable rule sets for multi-category classification of mixed data, competing with state-of-the-art methods.

Contribution

It introduces a novel inductive learning algorithm that produces explainable ASP rule sets for mixed data, combining efficiency with interpretability.

Findings

FOLD-RM achieves competitive accuracy with XGBoost and MLPs.

It outperforms XGBoost on large datasets.

Provides human-friendly explanations for predictions.

Abstract

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.