Abstract
The main objectives of data mining tasks involve extracting knowledge from data, which can be presented in the form of distributed local sources or centralized one. Inducing decision rules from one local data source is relatively straightforward. Nevertheless, obtaining a global model of rules based on different rule-based models is a more complicated task. In the paper, a new method for inducing decision rules from different sets of rules considered as data sources that are spread out is proposed. Each data source is characterized by a set of rules that are derived from the decision table using three different heuristics. To achieve a comprehensive model that represents the knowledge found within these different models, methods for global optimization relative to length and support are proposed. Experiments were performed on datasets from UCI Machine Learning Repository taking into account the characteristics of induced rule sets, i.e., their number, length and support, and classification accuracy. Constructed global rule-based models, taking into account average values, are comparable to the best results related to local rule-based models.
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Zielosko, B., Tetteh, E.T., Hunchak, D. (2023). Multi-heuristic Induction of Decision Rules. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_2
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