FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data

TL;DR

FOLD-R++ is an efficient, scalable, and explainable inductive learning algorithm for default rule induction from mixed data, outperforming some existing models on large datasets while maintaining interpretability.

Contribution

The paper introduces FOLD-R++, an improved version of FOLD-R, significantly enhancing scalability and efficiency without losing information, and integrates it with s(CASP) for explainable predictions.

Findings

FOLD-R++ outperforms FOLD-R in efficiency and scalability.

FOLD-R++ is competitive with XGBoost in performance but offers explainability.

FOLD-R++ surpasses RIPPER on large datasets.

Abstract

FOLD-R 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 classification tasks. We present an improved FOLD-R algorithm, called FOLD-R++, that significantly increases the efficiency and scalability of FOLD-R by orders of magnitude. FOLD-R++ improves upon FOLD-R without compromising or losing information in the input training data during the encoding or feature selection phase. The FOLD-R++ algorithm is competitive in performance with the widely-used XGBoost algorithm, however, unlike XGBoost, the FOLD-R++ algorithm produces an explainable model. FOLD-R++ is also competitive in performance with the RIPPER system, however, on large datasets FOLD-R++ outperforms RIPPER. We also create a powerful tool-set by combining FOLD-R++ with s(CASP)-a goal-directed ASP execution engine-to make predictions on new data samples using the answer set program generated by FOLD-R++. The s(CASP) system also produces a justification for the prediction. Experiments presented in this paper show that our improved FOLD-R++ algorithm is a significant improvement over the original design and that the s(CASP) system can make predictions in an efficient manner as well.