The paper introduces a novel hierarchical decision tree framework designed to enhance classification quality in dispersed and fragmented data. By integrating two levels of modeling, the framework employs local decision trees to generate prediction vectors, which are then synthesized through a global decision tree for final classification. Five distinct approaches – Tree, Tree & height, Bagging & stump, Bagging & height, and Bagging – are proposed and evaluated. Each method varies in how local models are constructed, focusing on factors such as tree depth, bagging methods, and tree stumps. Experimental results on data sets from the UCI Machine Learning Repository demonstrate that the Bagging approach, particularly with an optimized number of bags and trees height, consistently achieves superior performance across metrics including accuracy, F-measure, and balanced accuracy. These findings highlight the framework’s robustness and effectiveness in managing dispersed data, offering significant potential for applications in high-dimensional, fragmented and multi-class classification scenarios.