A Fast Learning Complex-valued Neural Classifier for real-valued classification problems

Abstract

This paper presents a fast learning fully complex-valued classifier to solve real-valued classification problems, called the `Fast Learning Complex-valued Neural Classifier' (FLCNC). The FLCNC is a single hidden layer network with a non-linear, real to complex transformed input layer, a hidden layer with a fully complex activation function and a linear output layer. The neurons in the input layer convert the real-valued input features to the Complex domain using an unique non-linear transformation. At the hidden layer, the complex-valued transformed input features are mapped onto a higher dimensional Complex plane using a fully complex-valued activation function of the type of `sech'. The parameters of the input and hidden neurons of the FLCNC are chosen randomly and the output parameters are estimated analytically which makes the FLCNC to perform fast classification. Moreover, the unique nonlinear input transformation and the orthogonal decision boundaries of the complex-valued neural network help the FLCNC to perform accurate classification. Performance of the FLCNC is demonstrated using a set of multi-category and binary real valued classification problems with both balanced and unbalanced data sets from the UCI machine learning repository. Performance comparison with existing complex-valued and real-valued classifiers show the superior classification performance of the FLCNC.