Knowledge Fusion by Harnessing Support Vector Machines for Collaborative Uncertain Data Classification in Multiagent Systems

Abstract

Distributed data mining (DDM) has emerged as a useful method for analyzing data that is spread across multiple sources. Nevertheless, DDM has other challenges that restrict its effectiveness, such as autonomy, privacy, efficiency, and implementation. DDM's rigidity and lack of adaptability may render it unsuitable for numerous applications due to its requirement for a consistent environment, administration, control, and categorization procedures. In order to address these challenges, we suggest the implementation of MAS-DDM, which combines a multiagent system (MAS) with DDM. MAS, or Multiagent Systems, is a methodology used to create independent agents that possess shared environments and can collaborate and communicate with one another. The study showcases the advantages and attractiveness of MAS-DDM. In the context of MAS-DDM, agents can exchange their thoughts, even when the data they possess is classified and cannot be disclosed. Other agents can then decide whether to incorporate these beliefs into their decision-making process, which may result in a revision of their initial assumptions about each data class. MAS-DDM focuses on the support vector machine (SVM) method, which is commonly employed for handling uncertain data. Our investigation demonstrates that the performance of MAS-DDM surpasses that of DDM strategies that do not incorporate communicative processes, even when all MAS-DDM agents utilize the same methodology. We present empirical evidence demonstrating that the precision of the categorization job is significantly enhanced through the exchange of knowledge among agents.