SVM-RFE-ED: A Novel SVM-RFE based on Energy Distance for Gene Selection and Cancer Diagnosis

Seyyid Ahmed Medjahed, Mohammed Ouali


Microarray expression data has been a very active research field and anindispensable tool for cancer diagnosis. The microarray expression datasetcontains thousands of genes and selecting a subset of informative genes is aprimordial 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 forclassification and does not take into account the ability of classseparability for each gene. In this paper, a novel SVM-RFE based on energydistance (ED) and called SVM-RFE-ED is proposed to overcome the limitation ofstandard SVM-RFE. The aims of our study are to achieve a high classificationaccuracy rate and improve the classification model. The experimentation isconducted on five widely used datasets. Experimental results indicate thatthe proposed approach SVM-RFE-ED provides good results and achieve a highclassification accuracy rate using a small number of genes.


feature selection; svm-rfe; energy distance; cancer diagnosis; microarray gene selection

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