Open AccessResearch on Feature Selection using SVM
Author(s) -
C. Amali Pushpam,
J. Jayanthi
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d5279.118419
Subject(s) - feature selection , support vector machine , computer science , classifier (uml) , artificial intelligence , data mining , selection (genetic algorithm) , machine learning , feature (linguistics) , statistical classification , pattern recognition (psychology) , linear classifier , philosophy , linguistics
A very fast and efficient classification algorithm is imperative to any application. Nowadays all kinds of applications produce a huge volume of data. Handling these 5’V characteristics data is really very crucial. While processing data, data classification simplifies the mission. Though many classification algorithms are available, they are not up to the mark to meet the fast growing challenges of current digital world. To fill this gap, feature selection is integrated with classifiers, as Feature selection has proved its impact on performance of classifiers. SVM is one of the most frequently used classifier. In this paper, different feature selection methods have been analyzed by studying 21 articles. This survey makes public that SVM based feature selection works better and widely used. Also in feature selection, filter method is widely used.