Feature selection and mass classification using particle swarm optimization and support vector machine

Man To Wong; Xiangjian He; Wei Chang Yeh; Zaidah Ibrahim; Zaidah Ibrahim; Yuk Ying Chung

doi:10.1007/978-3-319-12643-2_54

Feature selection and mass classification using particle swarm optimization and support vector machine

Man To Wong, Xiangjian He, Wei Chang Yeh, Zaidah Ibrahim, Zaidah Ibrahim, Yuk Ying Chung

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

1 Citation (Scopus)

Abstract

This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal breast tissue regions by using particle swarm optimization (PSO) based feature selection and Support Vector Machine (SVM). Twenty-three texture features were derived from the gray level co-occurrence matrix (GLCM) and gray level histogram of each ROI. PSO is used to search for the gamma and C parameters of SVM with RBF kernel which will give the best classification accuracy, using all the 23 features. Using the parameters of SVM found by PSO, PSO based feature selection is used to determine the significant features. Experimental results show that the proposed PSO based feature selection technique can find the significant features that can improve the classification accuracy of SVM. The proposed classification approach using PSO and SVM has better specificity and sensitivity when compared to other mass classification techniques.

Original language	English
Title of host publication	Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
Editors	Chu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh, Kaizhu Huang
Publisher	Springer Verlag
Pages	439-446
Number of pages	8
ISBN (Electronic)	9783319126425
DOIs	https://doi.org/10.1007/978-3-319-12643-2_54
Publication status	Published - 2014
Externally published	Yes
Event	21st International Conference on Neural Information Processing, ICONIP 2014 - Kuching, Malaysia Duration: 3 Nov 2014 → 6 Nov 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8836
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Neural Information Processing, ICONIP 2014
Country/Territory	Malaysia
City	Kuching
Period	3/11/14 → 6/11/14

Keywords

Feature selection
Mass classification
Particle swarm optimization
Support vector machine

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-12643-2_54

Cite this

Wong, M. T., He, X., Yeh, W. C., Ibrahim, Z., Ibrahim, Z., & Chung, Y. Y. (2014). Feature selection and mass classification using particle swarm optimization and support vector machine. In C. K. Loo, K. S. Yap, K. W. Wong, A. Teoh, & K. Huang (Eds.), Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings (pp. 439-446). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836). Springer Verlag. https://doi.org/10.1007/978-3-319-12643-2_54

Wong, Man To ; He, Xiangjian ; Yeh, Wei Chang et al. / Feature selection and mass classification using particle swarm optimization and support vector machine. Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. editor / Chu Kiong Loo ; Keem Siah Yap ; Kok Wai Wong ; Andrew Teoh ; Kaizhu Huang. Springer Verlag, 2014. pp. 439-446 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5c10fedde5984d2fbfbfcb7b26ffcd28,

title = "Feature selection and mass classification using particle swarm optimization and support vector machine",

abstract = "This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal breast tissue regions by using particle swarm optimization (PSO) based feature selection and Support Vector Machine (SVM). Twenty-three texture features were derived from the gray level co-occurrence matrix (GLCM) and gray level histogram of each ROI. PSO is used to search for the gamma and C parameters of SVM with RBF kernel which will give the best classification accuracy, using all the 23 features. Using the parameters of SVM found by PSO, PSO based feature selection is used to determine the significant features. Experimental results show that the proposed PSO based feature selection technique can find the significant features that can improve the classification accuracy of SVM. The proposed classification approach using PSO and SVM has better specificity and sensitivity when compared to other mass classification techniques.",

keywords = "Feature selection, Mass classification, Particle swarm optimization, Support vector machine",

author = "Wong, {Man To} and Xiangjian He and Yeh, {Wei Chang} and Zaidah Ibrahim and Zaidah Ibrahim and Chung, {Yuk Ying}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 21st International Conference on Neural Information Processing, ICONIP 2014 ; Conference date: 03-11-2014 Through 06-11-2014",

year = "2014",

doi = "10.1007/978-3-319-12643-2_54",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "439--446",

editor = "Loo, {Chu Kiong} and Yap, {Keem Siah} and Wong, {Kok Wai} and Andrew Teoh and Kaizhu Huang",

booktitle = "Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings",

address = "Germany",

}

Wong, MT, He, X, Yeh, WC, Ibrahim, Z, Ibrahim, Z & Chung, YY 2014, Feature selection and mass classification using particle swarm optimization and support vector machine. in CK Loo, KS Yap, KW Wong, A Teoh & K Huang (eds), Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8836, Springer Verlag, pp. 439-446, 21st International Conference on Neural Information Processing, ICONIP 2014, Kuching, Malaysia, 3/11/14. https://doi.org/10.1007/978-3-319-12643-2_54

Feature selection and mass classification using particle swarm optimization and support vector machine. / Wong, Man To; He, Xiangjian; Yeh, Wei Chang et al.
Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. ed. / Chu Kiong Loo; Keem Siah Yap; Kok Wai Wong; Andrew Teoh; Kaizhu Huang. Springer Verlag, 2014. p. 439-446 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836).

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

TY - GEN

T1 - Feature selection and mass classification using particle swarm optimization and support vector machine

AU - Wong, Man To

AU - He, Xiangjian

AU - Yeh, Wei Chang

AU - Ibrahim, Zaidah

AU - Chung, Yuk Ying

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal breast tissue regions by using particle swarm optimization (PSO) based feature selection and Support Vector Machine (SVM). Twenty-three texture features were derived from the gray level co-occurrence matrix (GLCM) and gray level histogram of each ROI. PSO is used to search for the gamma and C parameters of SVM with RBF kernel which will give the best classification accuracy, using all the 23 features. Using the parameters of SVM found by PSO, PSO based feature selection is used to determine the significant features. Experimental results show that the proposed PSO based feature selection technique can find the significant features that can improve the classification accuracy of SVM. The proposed classification approach using PSO and SVM has better specificity and sensitivity when compared to other mass classification techniques.

AB - This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal breast tissue regions by using particle swarm optimization (PSO) based feature selection and Support Vector Machine (SVM). Twenty-three texture features were derived from the gray level co-occurrence matrix (GLCM) and gray level histogram of each ROI. PSO is used to search for the gamma and C parameters of SVM with RBF kernel which will give the best classification accuracy, using all the 23 features. Using the parameters of SVM found by PSO, PSO based feature selection is used to determine the significant features. Experimental results show that the proposed PSO based feature selection technique can find the significant features that can improve the classification accuracy of SVM. The proposed classification approach using PSO and SVM has better specificity and sensitivity when compared to other mass classification techniques.

KW - Feature selection

KW - Mass classification

KW - Particle swarm optimization

KW - Support vector machine

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DO - 10.1007/978-3-319-12643-2_54

M3 - Conference contribution

AN - SCOPUS:84909990639

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 439

EP - 446

BT - Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings

A2 - Loo, Chu Kiong

A2 - Yap, Keem Siah

A2 - Wong, Kok Wai

A2 - Teoh, Andrew

A2 - Huang, Kaizhu

PB - Springer Verlag

T2 - 21st International Conference on Neural Information Processing, ICONIP 2014

Y2 - 3 November 2014 through 6 November 2014

ER -

Wong MT, He X, Yeh WC, Ibrahim Z, Ibrahim Z, Chung YY. Feature selection and mass classification using particle swarm optimization and support vector machine. In Loo CK, Yap KS, Wong KW, Teoh A, Huang K, editors, Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Springer Verlag. 2014. p. 439-446. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-12643-2_54

Feature selection and mass classification using particle swarm optimization and support vector machine

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