International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
In this paper, focuses on the classification problem in distributed data mining environments where the transfer of data between learning processes is limited. Existing solutions address this problem through the use of distributed technologies for applying data mining algorithms to learn global models from local learning processes. Multiagent based solutions that follow this approach overlook the autonomy of local learning processes, the decentralisation of system control, and the local learning heterogeneity of the processes. The authors propose a collaborative agent-based learning model inspired by an existing learning framework that overcomes these deficiencies by defining the overall learning process as a combination of local autonomous learners interacting with each other in order to improve their local classification performance.