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RuleKit

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 user-guided induction mode 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 under GNU AGPL-3.0 license.

RuleKit provides all the functionalities included in our previous packages:

• LR-Rules (Wróbel et al, 2017) for survival rules induction,
• GuideR (Sikora et al, 2019) for user-guided induction.

As these packages are no longer updated, please use RuleKit instead.

Prerequisites

The software requires Java Development Kit in version 11 to work properly. In Windows one can download the installer from Oracle webpage. In Linux, a system package manager should be used instead. For instance, in Ubuntu 18.04 execute the following command:

sudo apt-get install default-jdk

References

Gudyś, A, Sikora, M, Wróbel, Ł (2019) RuleKit: A Comprehensive Suite for Rule-Based Learning, arXiv

Sikora, M, Wróbel, Ł, Gudyś, A (2018) GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings, Knowledge-Based Systems, 173:1-14.

Wróbel, Ł, Gudyś, A, Sikora, M (2017) Learning rule sets from survival data, BMC Bioinformatics, 18(1):285.