# SCARI: Separate and Conquer Algorithm for Action Rules and Recommendations Induction

arXiv: https://arxiv.org/abs/2106.05348 · HTML: https://mlsanity.com/paper/2106.05348/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 77 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking](https://mlsanity.com/paper/1706.06691/) (2017)
- [RuleKit: A Comprehensive Suite for Rule-Based Learning](https://mlsanity.com/paper/1908.01031/) (2019)
- [GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings](https://mlsanity.com/paper/1806.01579/) (2018)

## Abstract (arXiv)

This article describes an action rule induction algorithm based on a sequential covering approach. Two variants of the algorithm are presented. The algorithm allows the action rule induction from a source and a target decision class point of view. The application of rule quality measures enables the induction of action rules that meet various quality criteria. The article also presents a method for recommendation induction. The recommendations indicate the actions to be taken to move a given test example, representing the source class, to the target one. The recommendation method is based on a set of induced action rules. The experimental part of the article presents the results of the algorithm operation on sixteen data sets. As a result of the conducted research the Ac-Rules package was made available.