A modular neural network architecture that selects a different set of features per module

Modular Neural Network (MNN) divides a problem into smaller and easier sub-problems, and each sub-problem is solved by a neural network called expert. In previous MNN architectures, all experts used the same set of features. This work proposes a modular neural network architecture in which a specialized set of features is selected per expert. As each expert deals with a different sub-problem, it is expected an improvement in the accuracy rate when different and specialized features are selected per expert. The feature selection procedure is an optimization method based on the binary particle swarm optimization. Experimental results over public datasets show that the proposed modular neural network obtains better accuracy rates than literature MNNs.