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

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

doi:10.1109/MLSP.2008.4685462

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 proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages	97-102
Number of pages	6
DOIs	https://doi.org/10.1109/MLSP.2008.4685462
State	Published - 2008
Event	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: Oct 16 2008 → Oct 19 2008

Publication series

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

Other

Other	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Country/Territory	Mexico
City	Cancun
Period	10/16/08 → 10/19/08

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Software
Electrical and Electronic Engineering

Access to Document

10.1109/MLSP.2008.4685462

Cite this

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). Article 4685462 (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685462

@inproceedings{f5e88dc2cd0342e68a57de33474fdf5d,

title = "SVM margin-based feature elimination applied to high-dimensional microarray gene expression data",

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.",

author = "Yanxin Zhang and Yaman Aksu and George Kesidis and Miller, {David J.} and Yue Wang",

year = "2008",

doi = "10.1109/MLSP.2008.4685462",

language = "English (US)",

isbn = "9781424423767",

series = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

pages = "97--102",

booktitle = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

note = "2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 ; Conference date: 16-10-2008 Through 19-10-2008",

}

Zhang, Y, Aksu, Y, Kesidis, G , Miller, DJ & 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., 4685462, Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, pp. 97-102, 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, Cancun, Mexico, 10/16/08. https://doi.org/10.1109/MLSP.2008.4685462

SVM margin-based feature elimination applied to high-dimensional microarray gene expression data. / Zhang, Yanxin; Aksu, Yaman; Kesidis, George et al.
Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008. 2008. p. 97-102 4685462 (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - SVM margin-based feature elimination applied to high-dimensional microarray gene expression data

AU - Zhang, Yanxin

AU - Aksu, Yaman

AU - Kesidis, George

AU - Miller, David J.

AU - Wang, Yue

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=58049180815&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=58049180815&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2008.4685462

DO - 10.1109/MLSP.2008.4685462

M3 - Conference contribution

AN - SCOPUS:58049180815

SN - 9781424423767

T3 - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

SP - 97

EP - 102

BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

T2 - 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Y2 - 16 October 2008 through 19 October 2008

ER -

Zhang Y, Aksu Y, Kesidis G , Miller DJ, Wang Y. 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. 2008. p. 97-102. 4685462. (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). doi: 10.1109/MLSP.2008.4685462

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

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this