The paper introduces a novel two-stage hierarchical framework for global classification from heterogeneous sources. In the first stage, local decision trees based on the CART algorithm generate probabilistic predictions for a shared validation set. These predictions are then fused using two alternative strategies: concatenation model, which preserves source-level distinctions, and averaging model, which emphasizes aggregations. In the second stage, a Neighborhood Rough Decision Tree (NRDT) serves as the global classifier. Unlike conventional decision trees, NRDT leverages rough set theory and adaptive neighborhood relations to handle continuous, probabilistic features without discretization, producing simpler and more interpretable rules. Experiments conducted on dispersed versions of three UCI datasets - Vehicle Silhouettes, Soybean (Large), and Lymphography - demonstrate that both fusion strategies achieve competitive performance across accuracy, F-measure, and balanced metrics, with no statistically significant difference between them. The proposed approach offers a robust, interpretable, and privacy-preserving solution for synthesizing knowledge in fragmented data environment.