A Fully Complex-valued Fast Learning Classifier (FC-FLC) for real-valued classification problems

Abstract

This paper presents a Fully Complex-valued Fast Learning Classifier (FC-FLC) to solve real-valued classification problems. FC-FLC is a single hidden layer network with a nonlinear input and hidden layer, and a linear output layer. The neurons at the input layer of the FC-FLC employ the circular transformation to convert the real-valued input features to the Complex domain. At the hidden layer, the complex-valued input features are projected onto a hyper-dimensional complex plane ( C m ? C K ) using the K hidden neurons employing Gudermannian (Gd) activation function. To investigate the suitability of the Gd as an activation function for a fully complex-valued network, we formulate the activation function in two forms. The output layer is linear. The input weights of the FC-FLC are chosen randomly and the output weights are estimated analytically. The best input weights corresponding to the best generalization performance of the FC-FLC are obtained by a k-fold cross validation. The performance of the proposed classifier is evaluated in comparison to other complex-valued and a few best performing real-valued classifiers on a set of benchmark classification problems from the UCI machine learning repository and a practical human emotion recognition problem. Statistical analysis is carried out to validate the performance of the classifier. Performance results show that FC-FLC has better classification ability than the other classifiers in the literature.