Work

RuleKit2: Faster and simpler rule learning

Year: 2025

Type: software-paper

Abstract: Rules offer an invaluable combination of predictive and descriptive capabilities. Our package for rule-based data analysis, RuleKit, has proven its effectiveness in classification, regression, and survival problems. Here we present its second version. New algorithms and optimized implementations of those previously included, significantly improved the computational performance of our suite, reducing the analysis time of some data sets by two orders of magnitude. The usability of RuleKit2 is provided by two new components: Python package and browser application with a graphical user interface. The former complies with scikit-learn , the most popular machine learning library for Python, allowing RuleKit2 to be straightforwardly integrated into existing data analysis pipelines. RuleKit2 is available at GitHub under GNU AGPL 3 license ( https://github.com/adaa-polsl/RuleKit ).

Source: SoftwareX

DOI: 10.1016/j.softx.2025.102289

Authors: Adam Gudyś, Cezary Maszczyk, Joanna Badura, Adam Grzelak, Marek Sikora, Łukasz Wróbel

Institutions: Silesian University of Technology, Łukasiewicz Research Network

Language: English

Topic: Data Mining Algorithms and Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funder: Silesian University of Technology

OpenAlex ID: W4413148221

Cites: 30

Cited by:

Related to: 10