Speed up SVM-RFE procedure using margin distribution

Yingqin Yuan, Leonid Hrebien, Moshe Kam

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

Abstract

In this paper, a new method is introduced to speed up the recursive feature ranking procedure by using the margin distribution of a trained SVM. The method, M-RFE, continuously eliminates features without retraining the SVM as long as the margin distribution of the SVM does not change significantly. Synthetic datasets and two benchmark microarray datasets were tested on M-RFE. Comparison with original SVM-RFE shows that our method speeds up the feature ranking procedure considerably with little or no performance degradation. Comparison of M-RFE to a similar speed up technique, E-RFE, provides similar classification performance, but with reduced complexity.

Original languageEnglish (US)
Title of host publication2005 IEEE Workshop on Machine Learning for Signal Processing
Pages297-302
Number of pages6
DOIs
StatePublished - Dec 1 2005
Externally publishedYes
Event2005 IEEE Workshop on Machine Learning for Signal Processing - Mystic, CT, United States
Duration: Sep 28 2005Sep 30 2005

Publication series

Name2005 IEEE Workshop on Machine Learning for Signal Processing

Other

Other2005 IEEE Workshop on Machine Learning for Signal Processing
Country/TerritoryUnited States
CityMystic, CT
Period9/28/059/30/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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