A multi-objective strategy in genetic algorithms for gene selection of gene expression data |
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Authors: | Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Muhammad Faiz Misman Michifumi Yoshioka |
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Affiliation: | (1) Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan;(2) Department of Software Engineering, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johore, Malaysia |
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Abstract: | A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used
to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer
classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes
relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification.
It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this
paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried
on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach
that used a single-objective strategy in a genetic algorithm.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 |
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Keywords: | Cancer classification Genetic algorithm Gene expression data Gene selection Multi-objective |
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