Objects Diversity and its Impact on Classification Quality in Dispersed Data Environments
Year: 2024
Type: article
Abstract: This paper studies a classification model dedicated to dispersed data, employing the k-nearest neighbors method as a base classifier and a 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 better on balanced 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.
Institution: University of Silesia in Katowice
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403503085