Best feature selection using successive elimination of poor performers

K. J. Siddiqui, E. C. Greco, N. Kadri, S. Mohiuddin, M. Sketch

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

1 Scopus citations

Abstract

This paper addresses the issue of feature extraction and selection, focusing particularly ion the feature selection issue. Without assuming any particular classification algorithm it suggests that first one should extract as much information (features) as conveniently possible and then apply the proposed successive elimination process to remove redundant and poor features and then select a significantly smaller, yet useful, feature subset that enhances the performance of the classifier. The algorithm is formally described and is successfully applied to a four class ECG classification problem. A minimum distance classifier (MDC) using Mahalanobis distance as the decision criterion is developed. Using MDC an overall recognition performance of 87.5% is obtained on the testing set of the four ECG classes.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference on Engineering in Medicine and Biology
EditorsAndrew Y.J. Szeto, Rangaraj M. Rangayyan
PublisherPubl by IEEE
Pages725-726
Number of pages2
Editionpt 2
ISBN (Print)0780313771
StatePublished - Dec 1 1993
EventProceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 2 (of 3) - San Diego, CA, USA
Duration: Oct 28 1993Oct 31 1993

Publication series

NameProceedings of the Annual Conference on Engineering in Medicine and Biology
Numberpt 2
Volume15
ISSN (Print)0589-1019

Other

OtherProceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 2 (of 3)
CitySan Diego, CA, USA
Period10/28/9310/31/93

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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