# FRRI: a novel algorithm for fuzzy-rough rule induction

arXiv: https://arxiv.org/abs/2403.04447 · HTML: https://mlsanity.com/paper/2403.04447/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 35 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [Statistically Testing Training Data for Unwanted Error Patterns using Rule-Oriented Regression](https://mlsanity.com/paper/2503.18497/) (2025)
- [Feature Subset Weighting for Distance-based Supervised Learning through Choquet Integration](https://mlsanity.com/paper/2504.00624/) (2025)
- [Optimising the attribute order in Fuzzy Rough Rule Induction](https://mlsanity.com/paper/2506.02805/) (2025)

## Abstract (arXiv)

Interpretability is the next frontier in machine learning research. In the search for white box models - as opposed to black box models, like random forests or neural networks - rule induction algorithms are a logical and promising option, since the rules can easily be understood by humans. Fuzzy and rough set theory have been successfully applied to this archetype, almost always separately. As both approaches to rule induction involve granular computing based on the concept of equivalence classes, it is natural to combine them. The QuickRulesciteJensenCornelis2009 algorithm was a first attempt at using fuzzy rough set theory for rule induction. It is based on QuickReduct, a greedy algorithm for building decision reducts. QuickRules already showed an improvement over other rule induction methods. However, to evaluate the full potential of a fuzzy rough rule induction algorithm, one needs to start from the foundations. In this paper, we introduce a novel rule induction algorithm called Fuzzy Rough Rule Induction (FRRI). We provide background and explain the workings of our algorithm. Furthermore, we perform a computational experiment to evaluate the performance of our algorithm and compare it to other state-of-the-art rule induction approaches. We find that our algorithm is more accurate while creating small rulesets consisting of relatively short rules. We end the paper by outlining some directions for future work.