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- W4382564454 abstract "The study concerns classification based on dispersed data, more specifically data collected independently in many local decision tables. The paper proposes an approach in which coalitions of local tables are generated using Pawlak’s conflict analysis model. Decision trees are built based on tables aggregated within the coalitions The paper examines the impact of the stop criterion (determined by the number of objects in a node) on the quality classification and on the rules’ quality generated by the model. The results are compared with the baseline approach, in which decision trees are built independently based on each of the decision tables. The paper shows that using the proposed model, the generated decision rules have much greater confidence than the rules generated by the baseline method. In addition, the proposed model gives better classification quality regardless of the stop criterion compared to the non-coalitions approach. Moreover, the use of higher values of the stop criterion for the proposed model significantly reduces the length of the rules while maintaining the classification accuracy and the rules’ confidence at a high level." @default.
- W4382564454 created "2023-06-30" @default.
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- W4382564454 date "2023-01-01" @default.
- W4382564454 modified "2023-09-27" @default.
- W4382564454 title "Rules’ Quality Generated by the Classification Method for Independent Data Sources Using Pawlak Conflict Analysis Model" @default.
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- W4382564454 doi "https://doi.org/10.1007/978-3-031-36027-5_30" @default.
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