Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm

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.

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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.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/1010761
Additional Information: Published in: Proceedings of the 11th UK Workshop on Computational Intelligence. Manchester : School of Computer Science, University of Manchester, 2011. http://ukci.cs.manchester.ac.uk/files/Proceedings.pdf
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 18 Jun 2013 10:02
Last Modified: 04 May 2020 20:23
URI: https://eprints.nottingham.ac.uk/id/eprint/2024

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