Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm
Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2011
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| Online Access: | https://eprints.nottingham.ac.uk/2024/ |
| _version_ | 1848790707116965888 |
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| author | Helmi, Hala Garibaldi, Jonathan M. Aickelin, Uwe |
| author_facet | Helmi, Hala Garibaldi, Jonathan M. Aickelin, Uwe |
| author_sort | Helmi, Hala |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the classification of microarray data. To prune the superfluous genes and samples we used a feature selection method called Recursive Feature Elimination (RFE), which is supposed to enhance the output of classification and avoid the local optimization problem. We examined the classification prediction accuracy of the TSVM-RFE algorithm in comparison with the Genetic Learning Across Datasets (GLAD) algorithm, as both are semi-supervised learning methods. Comparing these two methods, we found that the TSVM-RFE surpassed both a SVM using RFE and GLAD. |
| first_indexed | 2025-11-14T18:16:53Z |
| format | Conference or Workshop Item |
| id | nottingham-2024 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:16:53Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-20242020-05-04T20:23:55Z https://eprints.nottingham.ac.uk/2024/ Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm Helmi, Hala Garibaldi, Jonathan M. Aickelin, Uwe Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the classification of microarray data. To prune the superfluous genes and samples we used a feature selection method called Recursive Feature Elimination (RFE), which is supposed to enhance the output of classification and avoid the local optimization problem. We examined the classification prediction accuracy of the TSVM-RFE algorithm in comparison with the Genetic Learning Across Datasets (GLAD) algorithm, as both are semi-supervised learning methods. Comparing these two methods, we found that the TSVM-RFE surpassed both a SVM using RFE and GLAD. 2011 Conference or Workshop Item PeerReviewed Helmi, Hala, Garibaldi, Jonathan M. and Aickelin, Uwe (2011) Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm. In: UKCI 2011, 11th Annual Workshop on Computational Intelligence, 7-9 Sept 2011, Manchester, England. http://ukci.cs.manchester.ac.uk/files/Proceedings.pdf |
| spellingShingle | Helmi, Hala Garibaldi, Jonathan M. Aickelin, Uwe Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm |
| title | Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm |
| title_full | Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm |
| title_fullStr | Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm |
| title_full_unstemmed | Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm |
| title_short | Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm |
| title_sort | examining the classification accuracy of tsvms with feature selection in comparison with the glad algorithm |
| url | https://eprints.nottingham.ac.uk/2024/ https://eprints.nottingham.ac.uk/2024/ |