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Feature Subset Selection in Large Dimensionality using correlation based GA-SVM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 45 - Number 6 |
| Year of Publication: 2012 |
| Authors: Binita Kumari |
10.5120/6782-9084
|
Binita Kumari . Feature Subset Selection in Large Dimensionality using correlation based GA-SVM. International Journal of Computer Applications. 45, 6 ( May 2012), 5-8. DOI=10.5120/6782-9084
Abstract
The microarray can be used to measure the changes in the expression levels of thousands of genes simultaneously, to detect SNPs or to genotype or resequence mutant genomes. The high dimensional feature vectors of microarray impose a high dimensional cost as well as the risk of overfitting during classification. Feature selection is one way of reducing the dimensionality. Effective feature selection can be done based on correlation between attributes. In this paper, we introduce a correlation based wrapper algorithm for feature selection using genetic algorithm (GA) and Support Vector Machines with kernel functions for classification. We compare our approach with existing algorithm.
References
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- Li Zhuo, Jing Zheng, Fang Wang, A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine,In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
- Jianping Hua, Waibhav D. Tembe, and Edward R. Dougherty, "Performance of feature-selection methods in the classification of high-dimension data," , in Proc Pattern Recognition, 42 (2009) 409 -- 424.
- Iffat A. Gheyas, Leslie S. Smith, "Feature subset selection in large dimensionality domains", in Proc. Pattern Recognition, 43 (2010) 5 - 13
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