SVM margin-based feature elimination applied to high-dimensional microarray gene expression data

Yanxin Zhang, Yaman Aksu, George Kesidis, David J. Miller, Yue Wang

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

2 Scopus citations

Abstract

In this paper we investigate application of the recently developed margin-based feature elimination (MFE) method for feature selection in support vector machines to high-dimensional, small sample size data from the DNA microarray domain. We compared the performance of MFE to the well-known recursive feature elimination (RFE) method. Our results show that MFE outperforms RFE in terms of generalization accuracy and classifier margin, especially for low frequency of SVM retraining during the feature elimination process, which is practically necessitated for very high-dimensional feature spaces.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages97-102
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
Duration: Oct 16 2008Oct 19 2008

Publication series

NameProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Other

Other2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
CountryMexico
CityCancun
Period10/16/0810/19/08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Electrical and Electronic Engineering

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    Zhang, Y., Aksu, Y., Kesidis, G., Miller, D. J., & Wang, Y. (2008). SVM margin-based feature elimination applied to high-dimensional microarray gene expression data. In Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 (pp. 97-102). [4685462] (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685462