Multi Agents Classification System with Reliable Measure of Generalization

In this paper an efficient classification system using hierarchal multi_agent's technology based on neural network is introduced, where each agent implements as a neuraogy effort to o do thisl network (trained using back propagation learning algorithm). The system consist of two layers of agents, the top layer contain one agent works as control agent, its responsibility is to select the right agent from the agents in the lower layer to classify the related pattern depending on data’s features. Two techniques were used (regularization and earlier stopping criteria) to select the best one for each data set depending . The system provides a degree of generalization with the ability to improve it if there is a need for more generalization. The developed system was tested using different standard datasets obtained from the (University of California, Irvine) UCI Machine Learning Repository for the empirical analysis of machine learning algorithms. These are (User Knowledge Level, iris, and Banknote Authentication Dataset)