Abstract
This paper presents a novel approach to the classification of distributed data, which integratesthe cooperation of local decision tables within coalitions with rule induction and decision templates.The method aims to preserve model transparency while taking into account the diversityof data sources. Experiments were conducted on three datasets, comparing the performance offour rule induction algorithms: exhaustive search algorithm, genetic algorithm, covering algorithm,and LEM2. The best classification results were obtained for the exhaustive and geneticalgorithms, while the covering and LEM2 methods performed significantly worse. The proposedapproach achieves results comparable to the baseline method, which does not incorporate thecoalition mechanism, while offering higher interpretability. In addition, the proposed solutionwas compared with the Authors’ earlier approaches based on decision tree classifiers.
Paper Type
Full Paper
DOI
10.62036/ISD.2025.37
Coalition-Based Rule Induction and Decision Template Matching for Distributed Tabular Data
This paper presents a novel approach to the classification of distributed data, which integratesthe cooperation of local decision tables within coalitions with rule induction and decision templates.The method aims to preserve model transparency while taking into account the diversityof data sources. Experiments were conducted on three datasets, comparing the performance offour rule induction algorithms: exhaustive search algorithm, genetic algorithm, covering algorithm,and LEM2. The best classification results were obtained for the exhaustive and geneticalgorithms, while the covering and LEM2 methods performed significantly worse. The proposedapproach achieves results comparable to the baseline method, which does not incorporate thecoalition mechanism, while offering higher interpretability. In addition, the proposed solutionwas compared with the Authors’ earlier approaches based on decision tree classifiers.
Recommended Citation
Kusztal, K. & Przybyła-Kasperek, M. (2025). Coalition-Based Rule Induction and Decision Template Matching for Distributed Tabular DataIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.37