Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems
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 la...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/100129 http://hdl.handle.net/10220/13583 |
| _version_ | 1811696724753776640 |
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| author | Suresh, Sundaram Sundararajan, Narasimhan Savitha, R. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Suresh, Sundaram Sundararajan, Narasimhan Savitha, R. |
| author_sort | Suresh, Sundaram |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:43:55Z |
| format | Journal Article |
| id | ntu-10356/100129 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:43:55Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1001292020-05-28T07:17:43Z Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems Suresh, Sundaram Sundararajan, Narasimhan Savitha, R. School of Computer Engineering School of Electrical and Electronic Engineering 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. 2013-09-23T06:27:17Z 2019-12-06T20:17:10Z 2013-09-23T06:27:17Z 2019-12-06T20:17:10Z 2011 2011 Journal Article 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. https://hdl.handle.net/10356/100129 http://hdl.handle.net/10220/13583 10.1016/j.ins.2011.11.003 en Information sciences |
| spellingShingle | Suresh, Sundaram Sundararajan, Narasimhan Savitha, R. Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems |
| title | Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems |
| title_full | Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems |
| title_fullStr | Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems |
| title_full_unstemmed | Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems |
| title_short | Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems |
| title_sort | fast learning circular complex valued extreme learning machine cc elm for real valued classification problems |
| url | https://hdl.handle.net/10356/100129 http://hdl.handle.net/10220/13583 |
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