SCARI: Separate and Conquer Algorithm for Action Rules and Recommendations Induction
This work addresses the need for interpretable decision-making tools in domains like healthcare or marketing by providing a method to derive actionable insights from data, though it appears incremental as it builds on existing rule induction techniques.
The paper tackles the problem of inducing action rules and recommendations for moving examples between decision classes, presenting a sequential covering algorithm with two variants and a method for generating actionable recommendations. The algorithm was tested on sixteen datasets, resulting in the release of the Ac-Rules package.
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.