@inproceedings{894f688004a246fabc513aaacb1ac3ae,
title = "A model for gene selection and classification of gene expression data",
abstract = "Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data that will maximise the classification accuracy. A model for gene selection and classification has been developed by using a filter approach together with an improved hybrid of the genetic algorithm and a support vector machine classifier. It is shown that the classification accuracy of the proposed model is useful for the cancer classification of one that widely used gene expression benchmark data set.",
keywords = "Filter approach, Gene expression data, Gene selection, Hybrid approach",
author = "Mohamad, {M. S.} and S. Omatu and S. Deris and Hashim, {S. Z.M.}",
year = "2008",
language = "English",
isbn = "9784990288020",
series = "Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08",
pages = "320--323",
booktitle = "Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08",
note = "13th International Symposium on Artificial Life and Robotics, AROB 13th'08 ; Conference date: 31-01-2008 Through 02-02-2008",
}
