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

Authors

  • Seyyid Ahmed Medjahed University of Science and Technology Oran (USTO), Algeria
  • Mohammed Ouali Thales Canada Inc.

DOI:

https://doi.org/10.13053/cys-22-2-2819

Keywords:

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

Abstract

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.

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Published

2018-06-29

Issue

Vol. 22 No. 2 (2018): Thematic Issue on Numerical and Evolutionary Optimization (Guest editors: L. Trujillo, Y. Maldonado, O. Schuetze)

Section

Articles

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