SCARI: Separate and Conquer Algorithm for Action Rules and Recommendations Induction
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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.
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.
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References · 5
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2017Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweakingcited by 76
- 2019RuleKit: A Comprehensive Suite for Rule-Based Learningcited by 6
- 2018GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settingscited by 5
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