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https://hdl.handle.net/10356/100129
Title: | Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems | Authors: | Suresh, Sundaram Sundararajan, Narasimhan Savitha, R. |
Issue Date: | 2011 | Source: | Savitha, R., Suresh, S., & Sundararajan, N. (2011). Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems. Information sciences, 187, 277–290. | Series/Report no.: | Information sciences | Abstract: | In this paper, we present a fast learning fully complex-valued extreme learning machine classifier, referred to as ‘Circular Complex-valued Extreme Learning Machine (CC-ELM)’ for handling real-valued classification problems. CC-ELM is a single hidden layer network with non-linear input and hidden layers and a linear output layer. A circular transformation with a translational/rotational bias term that performs a one-to-one transformation of real-valued features to the complex plane is used as an activation function for the input neurons. The neurons in the hidden layer employ a fully complex-valued Gaussian-like (‘sech’) activation function. The input parameters of CC-ELM are chosen randomly and the output weights are computed analytically. This paper also presents an analytical proof to show that the decision boundaries of a single complex-valued neuron at the hidden and output layers of CC-ELM consist of two hyper-surfaces that intersect orthogonally. These orthogonal boundaries and the input circular transformation help CC-ELM to perform real-valued classification tasks efficiently. Performance of CC-ELM is evaluated using a set of benchmark real-valued classification problems from the University of California, Irvine machine learning repository. Finally, the performance of CC-ELM is compared with existing methods on two practical problems, viz., the acoustic emission signal classification problem and a mammogram classification problem. These study results show that CC-ELM performs better than other existing (both) real-valued and complex-valued classifiers, especially when the data sets are highly unbalanced. | URI: | https://hdl.handle.net/10356/100129 http://hdl.handle.net/10220/13583 |
DOI: | 10.1016/j.ins.2011.11.003 | Schools: | School of Computer Engineering School of Electrical and Electronic Engineering |
Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Journal Articles |
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