Radial Basis Function Cascade Correlation Networks

A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability to detect a novel cl...

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Bibliographic Details
Main Authors:	Peter de B. Harrington, Weiying Lu
Format:	Article
Language:	English
Published:	MDPI AG 2009-08-01
Series:	Algorithms
Subjects:	cascade correlation radial basis function artificial neural networks bootstrap Latin partition
Online Access:	http://www.mdpi.com/1999-4893/2/3/1045/

id	doaj-b149e107f6724b4b8f5c2787025dd6e0
record_format	Article
spelling	doaj-b149e107f6724b4b8f5c2787025dd6e02020-11-24T20:53:46ZengMDPI AGAlgorithms1999-48932009-08-01231045106810.3390/a2031045Radial Basis Function Cascade Correlation NetworksPeter de B. HarringtonWeiying LuA cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability to detect a novel class and an imbalanced class were demonstrated with synthetic data. In the chemical data sets, the growth regions of Italian olive oils were identified by their fatty acid profiles; mass spectra of polychlorobiphenyl compounds were classified by chlorine number. The prediction results by bootstrap Latin partition indicate that the proposed neural network is useful for pattern recognition. http://www.mdpi.com/1999-4893/2/3/1045/cascade correlationradial basis functionartificial neural networksbootstrap Latin partition
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Peter de B. Harrington Weiying Lu
spellingShingle	Peter de B. Harrington Weiying Lu Radial Basis Function Cascade Correlation Networks Algorithms cascade correlation radial basis function artificial neural networks bootstrap Latin partition
author_facet	Peter de B. Harrington Weiying Lu
author_sort	Peter de B. Harrington
title	Radial Basis Function Cascade Correlation Networks
title_short	Radial Basis Function Cascade Correlation Networks
title_full	Radial Basis Function Cascade Correlation Networks
title_fullStr	Radial Basis Function Cascade Correlation Networks
title_full_unstemmed	Radial Basis Function Cascade Correlation Networks
title_sort	radial basis function cascade correlation networks
publisher	MDPI AG
series	Algorithms
issn	1999-4893
publishDate	2009-08-01
description	A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability to detect a novel class and an imbalanced class were demonstrated with synthetic data. In the chemical data sets, the growth regions of Italian olive oils were identified by their fatty acid profiles; mass spectra of polychlorobiphenyl compounds were classified by chlorine number. The prediction results by bootstrap Latin partition indicate that the proposed neural network is useful for pattern recognition.
topic	cascade correlation radial basis function artificial neural networks bootstrap Latin partition
url	http://www.mdpi.com/1999-4893/2/3/1045/
work_keys_str_mv	AT peterdebharrington radialbasisfunctioncascadecorrelationnetworks AT weiyinglu radialbasisfunctioncascadecorrelationnetworks
_version_	1716796177685413888

Radial Basis Function Cascade Correlation Networks

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