Abstract

MEDJAHED, Seyyid Ahmed  and  OUALI, Mohammed. SVM-RFE-ED: A Novel SVM-RFE based on Energy Distance for Gene Selection and Cancer Diagnosis. Comp. y Sist. [online]. 2018, vol.22, n.2, pp.675-683.  Epub Jan 21, 2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-22-2-2819.

Microarray expression data has been a very active research field and an indispensable tool for cancer diagnosis. The microarray expression dataset contains thousands of genes and selecting a subset of informative genes is a primordial preprocessing step for improving the cancer classification. Support Vector Machine Recursive Feature Elimination (SVM-RFE) is one of the popular and effective gene selection approaches. However, SVM-RFE attempts to find the best possible combination for classification and does not take into account the ability of class separability for each gene. In this paper, a novel SVM-RFE based on energy distance (ED) and called SVM-RFE-ED is proposed to overcome the limitation of standard SVM-RFE. The aims of our study are to achieve a high classification accuracy rate and improve the classification model. The experimentation is conducted on five widely used datasets. Experimental results indicate that the proposed approach SVM-RFE-ED provides good results and achieve a high classification accuracy rate using a small number of genes.

Keywords : Cancer diagnosis; support vector machine; recursive feature elimination; gene selection; energy distance; classification.