Saini, Harsh and Lal, Sunil P. and Naidu, Vimal V. and Pickering, Vincel W. and Singh, Gurmeet and Tsunoda, Tatsuhiko and Sharma, Alokanand (2016) Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data. BMC Medical Genomics, 9 (3). pp. 74-83. ISSN 1755-8794
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Abstract
Background: High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy.
Methods: Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly
with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance.
Results: This technique was applied on publicly available datasets whereby it substantially reduced the number of
features used for classification while maintaining high accuracies.
Conclusion: The proposed technique can be extremely useful in feature selection as it heuristically removes
non-contributing features to improve the performance of classifiers.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > Q Science (General) R Medicine > R Medicine (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Fulori Nainoca - Waqairagata |
| Date Deposited: | 08 Mar 2017 04:41 |
| Last Modified: | 14 Oct 2020 02:44 |
| URI: | https://repository.usp.ac.fj/id/eprint/9665 |
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