Abstract
The paper introduces a novel approach for constructing a global model utilizing multilayer perceptron (MLP) neural networks and dispersed data sources. These dispersed data are independently gathered in various local tables, each potentially containing different objects and attributes, albeit with some shared elements (objects and attributes). Our approach involves the development of local models based on these local tables imputed with some artificial objects. Subsequently, local models are aggregated using weighted techniques. To complete, the global model is retrained using some global objects. In this study, the proposed method is compared with two existing approaches from the literature-homogeneous and heterogeneous multi-model classifiers. The analysis reveals that the proposed approach consistently outperforms these existing methods across multiple evaluation criteria including classification accuracy, balanced accuracy, F1-score, and precision. The results demonstrate that the proposed method significantly outperforms traditional ensemble classifiers and homogeneous ensembles of MLPs. Specifically, the proposed approach achieves an average classification accuracy improvement of 15% and a balanced accuracy enhancement of 12% over the baseline methods mentioned above. Moreover, in practical applications such as healthcare and smart agriculture, the model showcases superior properties by providing a single model that is easier to use and interpret. These improvements underscore the model's robustness and adaptability, making it a valuable tool for diverse real-world applications.