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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2009-08-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1999-4893/2/3/1045/ |
| _version_ | 1828764944038887424 |
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| author | Peter de B. Harrington Weiying Lu |
| author_facet | Peter de B. Harrington Weiying Lu |
| author_sort | Peter de B. Harrington |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T06:39:50Z |
| format | Article |
| id | doaj.art-b149e107f6724b4b8f5c2787025dd6e0 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-11T06:39:50Z |
| publishDate | 2009-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-b149e107f6724b4b8f5c2787025dd6e02022-12-22T01:17:15ZengMDPI 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 |
| 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 |
| title | 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_short | Radial Basis Function Cascade Correlation Networks |
| title_sort | radial basis function cascade correlation networks |
| 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 |