A Multiple classifier system for fast an accurate learning in neural network context

Nowadays, the Multiple Classification Systems (MCS) (also called as ensemble of classifiers, committee of learners and mixture of experts) constitutes a well-established research field in Pattern Recognition and Machine Learning. The MCS consists in dividing the whole problem with resampling methods, or using different models for constructing the system over a single data set. A similar approach is studied in the Neural Network context, with the Modular Neural Network. The main difference between these approaches is the processing cost associate to the training step of the Modular Neural Network (in its classical form), due to each module requires to be learned with the whole data set. In this paper, we analyze the performance of a Modular Neural Network and a Multiple Classifier System integrated by small Modular Neural Networks as individual member, in order to identity the convenience of each one. The experiments here were carried out on datasets from real problems showing the effectiveness of the Multiple Classifier System in terms of overall accuracy and processing time respect to uses a single Modular Neural Network.