# GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings

arXiv: https://arxiv.org/abs/1806.01579 · HTML: https://mlsanity.com/paper/1806.01579/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance](https://mlsanity.com/paper/2407.14543/) (2024)
- [Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival data](https://mlsanity.com/paper/2204.00497/) (2022)
- [SCARI: Separate and Conquer Algorithm for Action Rules and Recommendations Induction](https://mlsanity.com/paper/2106.05348/) (2021)
- [Criticality Analysis: Bio-inspired Nonlinear Data Representation](https://mlsanity.com/paper/2305.14361/) (2023)
- [RuleKit 2: Faster and simpler rule learning](https://mlsanity.com/paper/2504.20650/) (2025)

## Abstract (arXiv)

This article presents GuideR, a user-guided rule induction algorithm, which overcomes the largest limitation of the existing methods-the lack of the possibility to introduce user's preferences or domain knowledge to the rule learning process. Automatic selection of attributes and attribute ranges often leads to the situation in which resulting rules do not contain interesting information. We propose an induction algorithm which takes into account user's requirements. Our method uses the sequential covering approach and is suitable for classification, regression, and survival analysis problems. The effectiveness of the algorithm in all these tasks has been verified experimentally, confirming guided rule induction to be a powerful data analysis tool.