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

TL;DR

GuideR is a user-guided rule induction algorithm that incorporates domain knowledge into the rule learning process for classification, regression, and survival analysis, improving the relevance and usefulness of the resulting rules.

Contribution

It introduces a novel guided rule induction method that allows user preferences to influence rule learning across multiple problem types.

Findings

Effective in classification, regression, and survival tasks

Outperforms traditional automatic rule induction methods

Validated through extensive experiments

Abstract

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.