A Stable SVM-RFE Feature Selection Method for Gene Expression Data
Shaveta Tatwani1, Ela Kumar2

1Shaveta Tatwani*, Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi , India.
2Ela Kumar, Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2110-2115 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8482088619/2019©BEIESP | DOI: 10.35940/ijeat.F8482.088619
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Feature Selection techniques are generally employed to remove the inessential attributes before machine learning technique could be applied. It thus plays an extremely important role by eliminating the unnecessary features that do not contribute and sometimes degrade the performance and prediction accuracy of the machine learning technique. With the growth of dimensionality of data, Feature Selection becomes even more important because it helps to reduce the dimensions of data and hence decreases the requisite memory and computational complexity of the machine learning techniques. Support vector machine-recursive feature elimination (SVM-RFE) has proven to be an efficient wrapper feature selection technique which continues to be widely utilized in many applications, especially in classification of gene expression data. From the perspective of this data, not only the precision in classification but also the stability of Feature Selection method plays an important role. Nonetheless, the topic of stability is ignored in study of feature selection algorithms. To improve the stability of RFE method, a fusion of Information Gain and RFE (IG-RFE-SVM) method is proposed in this paper. Experimental studies show that IG-RFE-SVM outperforms SVM-RFE method in terms of stability.
Keywords: Feature Selection, Gene Expression Data, Machine Learning, Recursive Feature Elimination, Support Vector Machine.