Weighted Decision Aggregation for Dispersed Data in Unified and Diverse Coalitions

Dispersed data classification presents significant challenges due to structural variations, restricted information exchange, and the need for powerful decision-making strategies. This study introduces a dynamic classification system based on coalition formation using local models trained on independently collected local data. We explore two distinct coalition strategies: unified coalitions, which group models with similar prediction behaviors, and diverse coalitions, which aggregate models exhibiting contrasting decision tendencies. The impact of weighted and unweighted prediction aggregation is also examined to determine the influence of model reliability on global decision-making. Our framework uses Pawlak’s conflict analysis to form optimal coalitions. Experimental evaluations using multiple datasets demonstrate that coalition-based approaches significantly improve classification accuracy compared to operating individual models. The weighted diverse coalitions produce the most stable results. Statistical analyses confirm the effectiveness of the proposed methodology, highlighting the advantages of adaptive coalition strategies in dispersed environments.