Learning decision fusion in cooperative modular neural networks

The modular neural network offers several advantages over classical non-modular neural network approaches to complex pattern classification problems. However, the accuracy of the modular approach depends greatly on the accurate fusion of the individual classification decisions. The paper presents a method for improving the overall accuracy of modular neural networks by incorporating an adaptive decision fusion mechanism. The proposed algorithm offers significant improvement over typical modular networks by evolving a more informed decision fusion mechanism that can greatly improve the final classification decision for complex classification tasks