SVM-RFE-ED: A Novel SVM-RFE based on Energy Distance for Gene Selection and Cancer Diagnosis
Abstract: 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.
| Main Authors: | , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Instituto Politécnico Nacional, Centro de Investigación en Computación
2018
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| Online Access: | http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462018000200675 |
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