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

TL;DR

This paper introduces SCARI, a sequential covering algorithm for inducing action rules and recommendations, enabling decision-making support by moving instances between classes based on quality measures, validated on multiple datasets.

Contribution

It presents a novel sequential covering algorithm for action rule induction and a method for generating actionable recommendations from data.

Findings

Effective on sixteen datasets

Produced the Ac-Rules software package

Generated high-quality action rules

Abstract

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.