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

arXiv: https://arxiv.org/abs/2202.06913 · HTML: https://mlsanity.com/paper/2202.06913/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Inductive logic programming at 30: a new introduction](https://mlsanity.com/paper/2008.07912/) (2020)
- [Constraint Answer Set Programming without Grounding](https://mlsanity.com/paper/1804.11162/) (2018)
- [A New Algorithm to Automate Inductive Learning of Default Theories](https://mlsanity.com/paper/1707.02693/) (2017)
- [Justifications for Goal-Directed Constraint Answer Set Programming](https://mlsanity.com/paper/2009.10238/) (2020)
- [Heuristic Based Induction of Answer Set Programs: From Default theories to combinatorial problems](https://mlsanity.com/paper/1802.06462/) (2018)
- [Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming](https://mlsanity.com/paper/2109.08290/) (2021)

## Built on by (most cited first, 7 of 7)

- [Logic-Based Explainability in Machine Learning](https://mlsanity.com/paper/2211.00541/) (2022)
- [FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability](https://mlsanity.com/paper/2208.07912/) (2022)
- [Learning Brave Assumption-Based Argumentation Frameworks via ASP](https://mlsanity.com/paper/2408.10126/) (2024)
- [CON-FOLD -- Explainable Machine Learning with Confidence](https://mlsanity.com/paper/2408.07854/) (2024)
- [Counterfactual Explanation Generation with s(CASP)](https://mlsanity.com/paper/2310.14497/) (2023)
- [Counterfactual Generation with Answer Set Programming](https://mlsanity.com/paper/2402.04382/) (2024)
- [Explaining Weather Bulletins via ILP](https://mlsanity.com/paper/2607.21184/) (2026)

## Abstract (arXiv)

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.