# RRULES: An improvement of the RULES rule-based classifier

arXiv: https://arxiv.org/abs/2106.07296 · HTML: https://mlsanity.com/paper/2106.07296/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Built on by (most cited first, 1 of 1)

- [Optimal Pattern Detection Tree for Symbolic Rule-Based Classification](https://mlsanity.com/paper/2605.14374/) (2026)

## Abstract (arXiv)

RRULES is presented as an improvement and optimization over RULES, a simple inductive learning algorithm for extracting IF-THEN rules from a set of training examples. RRULES optimizes the algorithm by implementing a more effective mechanism to detect irrelevant rules, at the same time that checks the stopping conditions more often. This results in a more compact rule set containing more general rules which prevent overfitting the training set and obtain a higher test accuracy. Moreover, the results show that RRULES outperforms the original algorithm by reducing the coverage rate up to a factor of 7 while running twice or three times faster consistently over several datasets.