Journal
KNOWLEDGE AND INFORMATION SYSTEMS
Volume 26, Issue 3, Pages 487-500Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s10115-010-0288-x
Keywords
Feature selection; Genetic algorithm; MRMR; Support Vector Machine; Naive Bayes classifier; LOOCV
Funding
- Hassan II Academy of Science and Technology
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Gene expression data usually contain 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 discriminates 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 for tumor classification on five open datasets: NCI, Lymphoma, Lung, Leukemia and Colon using Support Vector Machine (SVM) and Na < ve Bayes (NB) 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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