Analysis and Testing of the m-Class RDP Neural Network

Abstract

The Recursive Deterministic Perceptron (RDP) feed-forward multilayer neural network is a generalisation of the single layer perceptron topology. This model is capable of solving any two-class classification problem unlike the single layer perceptron which can only solve classification problems dealing with linearly separable sets. For all classification problems, the construction of an RDP is done automatically and convergence is always guaranteed. A generalisation of the 2-class Recursive Deterministic Perceptron (RDP) exists. This generalisation always allows the deterministic separation of m-classes. It is based on a new notion of linear separability and it arises naturally from the 2 valued RDP. The methods for building 2-class RDP neural networks have been extensively tested. However, no testing has been done before on the m-class RDP method. For the first time, a study on the performance of the m-class method is presented. This study will allow the highlighting of the main advantages and disadvantages of this method by comparing the results obtained while building m-class RDP neural networks with other more classical methods such as Backpropagation and Cascade Correlation in terms of level of generalisation and topology size. The networks were trained and tested using the following standard benchmark classification datasets: Glass, Wine, Zoo, Iris, Soybean, and Wisconsin Breast Cancer.