CMNN: Cooperative Modular Neural Networks

A new cooperative modular neural network (CMNN) architecture for classification is introduced. The main idea is to decrease partial over- and under-learning by dealing with different levels of overlap in separate modules. Motivated by some basic biological modular-networks’ concepts, CMNN proposes a new cooperation scheme to integrate the information available at its modules. Cooperative modules utilize some voting techniques to come up with a collective decision. Moreover, the specialization concept is proposed as a solution for high overlap regions in the input space. A number of experiments which assess CMNN’s capabilities are outlined. The experiments compare it to several non-modular and modular state-of-the-art alternatives using several benchmark problems. The proposed modularization scheme proves to be an effective way to deal with the complexities of real classification problems.