Classification using redundant mapping in modular neural networks

Classification is a major problem of study that involves formulation of decision boundaries based on the training data samples. The limitations of the single neural network approaches motivate the use of multiple neural networks for solving the problem in the form of ensembles and modular neural networks. While the ensembles solve the problem redundantly, the modular neural networks divide the computation into multiple modules. The modular neural network approach is used where a Self Organizing Map (SOM) selects the module which would perform the computation of the output, whenever any input is given. In the proposed architecture, the SOM selects multiple modules for problem solving, each of which is a neural network. Then the multiple selected neural networks are used redundantly for computing the output. Each of the outputs is integrated using an integrator. The proposed model is applied to the problem of Breast Cancer diagnosis, whose database is made available from the UCI Machine Learning Repository. Experimental results show that the proposed model performs better than the conventional approaches.