Abstract:
Support Vector Machine (SVM) is the most widely used classifier for performing the classification of a massive dataset. This research paper aims to improve the feature se...Show MoreMetadata
Abstract:
Support Vector Machine (SVM) is the most widely used classifier for performing the classification of a massive dataset. This research paper aims to improve the feature selection and classify the gene expression data by using the SVM classifier. And also aim to decrease the computational time of the SVM-RFE (support vector machine recursive feature elimination) algorithm by identifying more than one redundant genes and removing them in every iteration. Most of the gene expression profile contains an enormous number of features with few numbers of samples, to reduce the number of features before applying to the classifier for performing the classification; a feature selection algorithm is needed. The most effective algorithm used to perform the feature selection of microarray is, SVM-RFE. On every iteration, it generates the rank of the features and removes the very least ranked feature, which is the most irrelevant. Since the modified algorithm is used to remove more than one redundant features in every iteration. It will help to reduce the computational time and increase the accuracy prediction.
Published in: 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)
Date of Conference: 05-06 July 2019
Date Added to IEEE Xplore: 13 February 2020
ISBN Information: