Gene Selection using a Hybrid RFE Along with LASSO for Cancer Classification
M. J. Abinash1, V. Vasudevan2
1Abinash, Department of Information Technology, Kalasalingam Academy of Research and Education, Srivilliputhur (Tamil Nadu), India.
2Vasudevan, Department of Information Technology, Kalasalingam Academy of Research and Education, Srivilliputhur (Tamil Nadu), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 83-86 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A10961291S419/19©BEIESP | DOI: 10.35940/ijeat.A1096.1291S419
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© 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: Gene expression profiling using microarray technology has done with the chip based phenomena. For studying gene expression data are more helpful in knowing various diseases and more useful in finding diseases. Recently in the bioinformatics field, cancer prediction using gene expression data had made the assuring area. Samples having the gene attributes will not surely give the efficient amount of classification. Overcoming these contribution, a strong method is required for selecting the relevant gene features for building the classification model effectively. Basically least absolute shrinkage and selection operator (LASSO) and Recursive feature elimination (RFE) are automatic gene feature selection methods used for classification. Here in our proposed work, we use these two methods as a hybrid one for selecting the features and later it applied into the Support vector machine (SVM) for easy classification. It made best when compared to the existing techniques by their performance measures, were regulated on six publically available cancer datasets. Just out it gives the good awareness in the selection of features.
Keywords: LASSO, Gene Selection, RFE, SVM, Cancer Classification.
Scope of the Article: Classification