Sharma, Alokanand and Paliwal, K.K. (2008) Cancer classification by gradient LDA technique using microarray gene expression data. Data & Knowledge Engineering, 66 (2). pp. 338-347. ISSN 0169-023X
Full text not available from this repository.Abstract
Cancer classification is one of the major applications of the microarray technology. When standard machine learning techniques are applied for cancer classification, they face the small sample size (SSS) problem of gene expression data. The SSS problem is inherited from large dimensionality of the feature space (due to large number of genes) compared to the small number of samples available. In order to overcome the SSS problem, the dimensionality of the feature space is reduced either through feature selection or through feature extraction. Linear discriminant analysis (LDA) is a well-known technique for feature extraction-based dimensionality reduction. However, this technique cannot be applied for cancer classification because of the singularity of the within-class scatter matrix due to the SSS problem. In this paper, we use Gradient LDA technique which avoids the singularity problem associated with the within-class scatter matrix and shown its usefulness for cancer classification. The technique is applied on three gene expression datasets; namely, acute leukemia, small round blue-cell tumour (SRBCT) and lung adenocarcinoma. This technique achieves lower misclassification error as compared to several other previous techniques.
| Item Type: | Journal Article |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Ms Neha Harakh |
| Date Deposited: | 10 Jan 2008 22:53 |
| Last Modified: | 18 Jul 2012 02:03 |
| URI: | http://repository.usp.ac.fj/id/eprint/216 |
Actions (login required)
 |
View Item |