Exploring the Impact of Object Diversity on Classification Quality in Dispersed Data Environments

This paper studies a classification model dedicated to dispersed data, employing the \(k-\) nearest neighbors method as a base classifier and the Radial Basis Function (RBF) network as a fusion method. Focusing on classifying objects described by different attributes, the study systematically reduces common objects in local tables to assess the robustness of the model. Surprisingly, the proposed approach shows resilience in reducing common objects without significantly affecting key metrics such as F-measure, balanced accuracy and overall accuracy. Moreover, the studied model performs exceptionally well on imbalanced data. This research contributes valuable insights into dispersed data classification, demonstrating the model’s effectiveness in handling diverse objects and attributes. The findings have implications for fields reliant on dispersed data storage, such as healthcare, banking, and surveillance, showcasing the model’s potential for real-world applications.