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

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

36 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 Sept 20177 Sept 2017

Conference

Conference2017 International Conference on Computer and Applications
Country/TerritoryUnited 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

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