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Algorithms 2009, 2(3), 1045-1068; doi:10.3390/a2031045
Article

Radial Basis Function Cascade Correlation Networks

 and
*
Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701, USA
* Author to whom correspondence should be addressed.
Received: 1 July 2009 / Revised: 31 July 2009 / Accepted: 21 August 2009 / Published: 27 August 2009
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
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Abstract

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.
Keywords: cascade correlation; radial basis function; artificial neural networks; bootstrap Latin partition cascade correlation; radial basis function; artificial neural networks; bootstrap Latin partition
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lu, W.; Harrington, P.B. Radial Basis Function Cascade Correlation Networks. Algorithms 2009, 2, 1045-1068.

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