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 plugin, as well as R package and Python packages. A documented Java API is also provided for convenience.

RuleKit provides latest versions of our algorithms (some of them were initially published as independent packages and integrated later):

• LR-Rules (Wróbel et al, 2017) - survival rules induction,
• GuideR (Sikora et al, 2019) - user-guided induction.
• RuleKit-CS (Gudyś et al, 2024) - contrast set mining.

Prerequisites

The software requires Java Development Kit in version 8 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 16.04 execute the following command:

sudo apt-get install default-jdk

References

Gudyś, A, Sikora, M, Wróbel, Ł (2024) Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival data, Expert Systems with Applications, 248: 123376

Gudyś, A, Sikora, M, Wróbel, Ł (2020) RuleKit: A comprehensive suite for rule-based learning, Knowledge-Based Systems, 194: 105480

Sikora, M, Wróbel, Ł, Gudyś, A (2019) 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.