GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings. 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. -- UPDATE: GuideR is now the part of RuleKit - our comprehensive suite for rule-based learning. We suggest using RuleKit for analyses as we constatly improve its functionality and keep it up to date.
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References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Stańczyk, U.; Zielosko, B.: Heuristic-based feature selection for rough set approach (2020)
- Adam Gudyś, Marek Sikora, Łukasz Wróbel: RuleKit: A Comprehensive Suite for Rule-Based Learning (2019) arXiv
- Marek Sikora, Łukasz Wróbel, Adam Gudyś: GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings (2018) arXiv