Feature Selection For Genomic Data By Combining Filter And Wrapper Approaches
Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. In this paper, we propose a two-stage selection algorithm for genomic...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | Brasil |
| Recursos: | Universidade Federal de Lavras (UFLA) |
| Repositorio: | INFOCOMP: Jornal de Ciência da Computação |
| Idioma: | inglés |
| OAI Identifier: | oai:infocomp.dcc.ufla.br:article/279 |
| Acesso em linha: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/279 |
| Access Level: | acceso abierto |
| Palavra-chave: | Feature selection Genetic algorithm MRMR Support Vector Machine Naïve Bayes classi- fier LOOCV |
| Resumo: | Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. In this paper, we propose a two-stage selection algorithm for genomic data by combining MRMR (Minimum Redundancy Maximum Relevance) and GA (Genetic Algorithm): In the first stage, MRMR is used to filter noisy and redundant genes in high dimensional microarray data. In the second stage, the GA uses the classifier accuracy as a fitness function to select the highly discriminating genes. The proposed method is tested on five open datasets: NCI, Lymphoma, Lung, Leukemia and Colon using Support Vector Machine and Naïve Bayes classifiers. The comparison of the MRMR-GA with MRMR filter and GA wrapper shows that our method is able to find the smallest gene subset that gives the most classification accuracy in leave-one-out cross-validation (LOOCV). |
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