CMNN: cooperative modular neural network

The current generation of nonmodular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks´ hidden nodes. We propose the Cooperative Modular Neural Network (CMNN) architecture, which deals with different levels of overlap in different modules. The modules share their information and cooperate in taking a global classification decision through voting. Moreover, special modules are dedicated to resolve high overlaps in the input-space. The performance of the new model outperforms that of the nonmodular alternative when when applied to ten famous benchmark classification problems