Empirical Evaluation of the Performance of Feature Selection Approaches on Random Forest

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Medical data contain very valuable information which can save many lives if it is analyzed and utilized efficiently. Efficient analysis of this large volume of data demands the right choice of predictors and this in turn can impact the accuracy of the decision support system. Dimensionality reduction and feature subset selection are two techniques to reduce the number of features used in classification. In this paper we perform an empirical evaluation of four feature selection methods when applied in conjunction with Random Forest classifier. The feature selection techniques applied are Relief feature selection algorithm, Random forest selector, Recursive feature elimination and Boruta Feature selection algorithm. Results show that feature selection methods boosts the performance of the classifiers and in this case the features selected by the Boruta feature selection algorithm gives the best results.

Original languageEnglish
Title of host publication2017 International Conference on Computer and Applications (ICCA)
PublisherIEEE
Pages227-231
Number of pages5
ISBN (Electronic)9781538627525
DOIs
Publication statusPublished - 23 Oct 2017
Event2017 International Conference on Computer and Applications - Doha, United Arab Emirates
Duration: 6 Sep 20177 Sep 2017

Conference

Conference2017 International Conference on Computer and Applications
CountryUnited Arab Emirates
CityDoha
Period6/09/177/09/17

Keywords

  • Classification
  • Data mining
  • Feature selection
  • Heart disease
  • Random Forest

ASJC Scopus subject areas

  • Software
  • Control and Optimization
  • Education
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Empirical Evaluation of the Performance of Feature Selection Approaches on Random Forest'. Together they form a unique fingerprint.

Cite this