Scalable data fusion via a scale-based hierarchical framework: Adapting to multi-source and multi-scale scenarios

Multi-source information fusion addresses challenges in integrating and transforming complementary data from diverse sources to facilitate unified information representation for centralized knowledge discovery. However, traditional methods face difficulties when applied to multi-scale data, where optimal scale selection can effectively resolve these issues but typically lack the advantage of identifying the optimal and simplest data from different data source relationships. Moreover, in multi-source, multi-scale environments, heterogeneous data (where identical samples have different features and scales in different sources) is prone to occur. To address these challenges, this study proposes a novel approach in two key stages: first, aggregating heterogeneous data sources and refining datasets using information gain; second, employing a customized Scale-based Tree (SbT) structure for each attribute to help extract specific scale information value, thereby achieving effective data fusion goals. Extensive experimental evaluations cover ten different datasets, reporting detailed performance across multiple metrics including Approximation Precision (AP), Approximation Quality (AQ) values, classification accuracy, and computational efficiency. The results highlight the robustness and effectiveness of our proposed algorithm in handling complex multi-source, multi-scale data environments, demonstrating its potential and practicality in addressing real-world data fusion challenges.