RuleKit: A Comprehensive Suite for Rule-Based Learning

About

Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induction facilitates verifying hypotheses concerning data dependencies which are expected or of interest. The powerful and flexible experimental environment allows straightforward investigation of different induction schemes. The analysis can be performed in batch mode, through RapidMiner plug-in, or R package. A documented Java API is also provided for convenience. The software is publicly available at GitHub under GNU AGPL-3.0 license.

Adam Gudyś, Marek Sikora, \Lukasz Wróbel · 2019

Related benchmarks

Task	Dataset	Result
Subgroup Discovery	Nwtco SurvSet	Objective Value213.8	8
Subgroup Discovery	Dialysis SurvSet	Our Objective Score4.7	4
Survival Subgroup Discovery	NWTCO	Runtime (s)2.59	4
Survival Subgroup Discovery	Rott2	Runtime (s)3.2	4
Survival Subgroup Discovery	rdata	Runtime (seconds)0.46	4
Survival Subgroup Discovery	Aids2	Runtime (seconds)2.55	4
Survival Subgroup Discovery	Dialysis	Runtime (seconds)65.11	4
Survival Subgroup Discovery	TRACE	Runtime (s)1.08	4
Survival Subgroup Discovery	SUPPORT2	Runtime (seconds)3.86	4
Survival Subgroup Discovery	DataDIVAT2	Runtime (seconds)0.61	4