A New Gene Selection Method Based on Maximum Correlation and Minimum Redundancy

M. Ebrahimpour(1*), H. Mahmoodian(2), R. Ghayour(3)

(1) Department of Electrical Engineering, Science and Research branch, Islamic Azad University, Iran, Islamic Republic of
(2) Faculty of Electrical Engineering, Najafabad branch, Islamic Azad University, Iran, Islamic Republic of
(3) Department of Electrical Engineering, Science and Research branch, Islamic Azad University, Iran, Islamic Republic of
(*) Corresponding author


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Abstract


Microarray technology is a powerful tool for analyzing the behavior of thousands of genes simultaneously, and plays an important role in diagnosis, detection and treatment methods. Standard statistical methods are not suited to classification and diagnosis, when the number of samples is greater than the number of genes. Reducing the size of the selected set of genes with high potential for classification of microarray data analysis is thus an important goal. In this paper, we propose a new feature selection method based on maximum correlation and minimum redundancy (MCMR). We evaluate the performance of MCMR using three microarray data sets: the colon cancer data, breast cancer data and the DLBCL dataset. In general, MCMR can significantly reduce the number of genes and perform better than SNR, PCC and Fisher score.
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Keywords


Gene Selection; Classification; Correlation; Redundancy

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