Designing a New Multilayer Feedforward Modular Network for Classification Problems
Joaquín Torres-Sospedra, Carlos Hernández-Espinosa, Mercedes Fernández-Redondo · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
There are two different ways to create a Multiple Classification System based on neural networks. The first one is the Ensemble approach; it consists on combining the outputs of different networks which solve the same problem in a suitable manner to give a single output. The second one is the Modular approach; it consists on decomposing the problem into subproblems, the final decision is taken with the information provided by the networks. One of the most known methods to build a Modular Neural Network is the Mixture of Neural Networks. In this paper we present a Mixture of Multilayer Feedforward Networks a modular method based on Multilayer Feedforward networks. Finally, we have included a comparison among Simple Ensemble, Mixture of Neural Networks and Mixture of Multilayer Feedforward Networks. We have tested the methods with eight databases from the UCI repository and the results show that Mixture of Multilayer Feedforward Networks is the best performing method.