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

作者：Sivachitra M^*; Savitha R; Suresh S; Vijayachitra S

来源：Neurocomputing, 2015, 149: 198-206.

DOI：10.1016/j.neucom.2014.04.075

• Complex-valued neural network
• Classification
• Codermannian function
• Circular transformation
• Extreme learning machine

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 complexvalued input features are projected onto a hyper-dimensional complex plane (Cm) 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 realvalued 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.

• 出版日期2015-2-3
• 单位南阳理工学院