Ensembles of random trees with coalitions - a classification model for dispersed data
Małgorzata Przybyła‐Kasperek, Jakub Sacewicz · Procedia Computer Science · 2024
The paper delves into the challenge of classification using dispersed data gathered from independent sources. The examined approach involves local models as ensembles of random trees constructed based on local data and randomly selected attributes. In the proposed model, a conflict analysis is used to identify the coalitions of local models. Finally, four different strategies for generating final decisions are explored: utilizing coalitions with and without weights and allowing one or two of the strongest coalitions to make decisions. The paper demonstrates that, regardless of the chosen method for making the final decision, the proposed model with conflict analysis and coalitions obtained better results, particularly in terms of the F1 measure and accuracy, compared to approaches from the literature, such as random forest or a single tree generated based on each local table separately with majority voting.