Atmospheric corrosion modelling with SVM based feature selection

Zhenduo Fu; Dongmei Fu; Xiaogang Li

doi:10.1109/CISE.2009.5365365

Atmospheric corrosion modelling with SVM based feature selection

Zhenduo Fu^*
, Dongmei Fu
, Xiaogang Li

^*Corresponding author for this work

University of Science and Technology Beijing

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

5 Scopus citations

Abstract

Atmospheric corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the feature selection of a small subset of several important environmental factors from many relevant ones. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing, we introduce the novel method of feature selection based on Support Vector Machine (SVM). We demonstrate experimentally that the factors selected by this algorithm yield better modelling precision. Least Square (LS) based feature selection method is also included in our experiment to reveal the superiority of SVM algorithm in our problem.

Original language	English
Title of host publication	Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009
DOIs	https://doi.org/10.1109/CISE.2009.5365365
State	Published - 2009
Externally published	Yes
Event	2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009 - Wuhan, China Duration: 11 Dec 2009 → 13 Dec 2009

Publication series

Name	Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

Conference

Conference	2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009
Country/Territory	China
City	Wuhan
Period	11/12/09 → 13/12/09

Keywords

Atmospheric corrosion
SVM-RFE
Small sample size

Access to Document

10.1109/CISE.2009.5365365

Cite this

Fu, Z., Fu, D., & Li, X. (2009). Atmospheric corrosion modelling with SVM based feature selection. In Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009 Article 5365365 (Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009). https://doi.org/10.1109/CISE.2009.5365365

@inproceedings{31a3b40e2e14472f81e9cb6d8d9f69e3,

title = "Atmospheric corrosion modelling with SVM based feature selection",

abstract = "Atmospheric corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the feature selection of a small subset of several important environmental factors from many relevant ones. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing, we introduce the novel method of feature selection based on Support Vector Machine (SVM). We demonstrate experimentally that the factors selected by this algorithm yield better modelling precision. Least Square (LS) based feature selection method is also included in our experiment to reveal the superiority of SVM algorithm in our problem.",

keywords = "Atmospheric corrosion, SVM-RFE, Small sample size",

author = "Zhenduo Fu and Dongmei Fu and Xiaogang Li",

year = "2009",

doi = "10.1109/CISE.2009.5365365",

language = "英语",

isbn = "9781424445073",

series = "Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009",

booktitle = "Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009",

note = "2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009 ; Conference date: 11-12-2009 Through 13-12-2009",

}

Fu, Z, Fu, D & Li, X 2009, Atmospheric corrosion modelling with SVM based feature selection. in Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009., 5365365, Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009, 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009, Wuhan, China, 11/12/09. https://doi.org/10.1109/CISE.2009.5365365

Atmospheric corrosion modelling with SVM based feature selection. / Fu, Zhenduo; Fu, Dongmei; Li, Xiaogang.
Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009. 2009. 5365365 (Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009).

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

TY - GEN

T1 - Atmospheric corrosion modelling with SVM based feature selection

AU - Fu, Zhenduo

AU - Fu, Dongmei

AU - Li, Xiaogang

PY - 2009

Y1 - 2009

N2 - Atmospheric corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the feature selection of a small subset of several important environmental factors from many relevant ones. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing, we introduce the novel method of feature selection based on Support Vector Machine (SVM). We demonstrate experimentally that the factors selected by this algorithm yield better modelling precision. Least Square (LS) based feature selection method is also included in our experiment to reveal the superiority of SVM algorithm in our problem.

AB - Atmospheric corrosion has caused more and more losses and costs these years, so the world begin to pay much attention to this problem. In this paper, we mainly discuss the feature selection of a small subset of several important environmental factors from many relevant ones. With our experimental data with very small sample size, a model of corrosion rate is built. After specialized data preprocessing, we introduce the novel method of feature selection based on Support Vector Machine (SVM). We demonstrate experimentally that the factors selected by this algorithm yield better modelling precision. Least Square (LS) based feature selection method is also included in our experiment to reveal the superiority of SVM algorithm in our problem.

KW - Atmospheric corrosion

KW - SVM-RFE

KW - Small sample size

UR - https://www.scopus.com/pages/publications/77949661781

U2 - 10.1109/CISE.2009.5365365

DO - 10.1109/CISE.2009.5365365

M3 - 会议稿件

AN - SCOPUS:77949661781

SN - 9781424445073

T3 - Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

BT - Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

T2 - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

Y2 - 11 December 2009 through 13 December 2009

ER -

Atmospheric corrosion modelling with SVM based feature selection

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Keywords

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