Research on Feature Selection using SVM
C. Amali Pushpam1, J. Gnana Jayanthi2
1C.Amali Pushpam, Research Scholar, Rajah Serfoji College, (Affiliated to Bharathidasan University), Tamil Nadu, India.
2J.Gnana Jayanthi, Assistant Professor, Department of Computer Science, Rajah Serfoji College, (Affiliated to Bharathidasan University), Tamil Nadu, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7252-7256 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5279118419/2019©BEIESP | DOI: 10.35940/ijrte.D5279.118419

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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 (

Abstract: 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.
Keywords: Feature Selection, Classifier, Support Vector Machine, Ensemble.
Scope of the Article: Classification.