Support Vector Machine Classifier with WHM Offset for Unbalanced Data

Boyang Li; Jinglu Hu; Kotaro Hirasawa

doi:10.20965/jaciii.2008.p0094

single-jc.php

« previous

JACIII Vol.12 No.1 pp. 94-101

doi: 10.20965/jaciii.2008.p0094

(2008)

Paper:

Views over last 60 days: 447

Support Vector Machine Classifier with WHM Offset for Unbalanced Data

Boyang Li, Jinglu Hu, and Kotaro Hirasawa

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan

Received:

May 30, 2007

Accepted:

October 18, 2007

Published:

January 20, 2008

Keywords:

SVM, unbalanced data, WHM offset, classification, boundary excursion

Abstract

We propose an improved support vector machine (SVM) classifier by introducing a new offset, for solving the real-world unbalanced classification problem. The new offset is calculated based on the unbalanced support vectors resulting from the unbalanced training data. We developed a weighted harmonic mean (WHM) algorithm to further reduce the effects of noise on offset calculation. We apply the proposed approach to classify real-world data. Results of simulation demonstrate the effectiveness of our proposed approach.

Cite this article as:

B. Li, J. Hu, and K. Hirasawa, “Support Vector Machine Classifier with WHM Offset for Unbalanced Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.1, pp. 94-101, 2008.

Data files:

References

[1] N. Cristianini and J. Shawe-Taylor, “An Introduction to Support Vector Machines,” Cambridge, UK, Cambridge Univ. Press, 2000.
[2] P. Bartlett and J. Shawe-Taylor, “Generalization performance of support vector machines and other pattern classifiers,” In Advances in Kernel Methods support Vector Learning, Cambridge, MA, MIT Press, 1998.
[3] P. Xu and A. K. Chan, “Support vector machines for multi-class signal classification with unbalanced samples,” In Neural Networks, Proc. of the Int. Joint Conf. on Vol.2, Issue, pp. 20-24, July, 2003.
[4] P. Xu and A. K. Chan, “Support vector machines for multi-class signal classification with unbalanced samples,” In Proc. of the Int. Joint Conf. on Neural Networks, 2003.
[5] T. Eitrich and B. Lang, “Efficient optimization of support vector machine learning parameters for unbalanced datasets,” In Journal of Computational and Applied Mathematics, Vol.196, Issue 2, pp. 425-436, 2006.
[6] S.-X. Du and S.-T. Chen, “Weighted Support Vector Machine for Classification,” In IEEE Int. Conf. on Systems, Man and Cybernetics, 2005.
[7] J. Huang, J. Lin, X. He, and M. Dai, “The Algorithm for Detecting Hiding Information Based on SVM,” In Advances in Neural Networks - ISNN 2004, 2004.
[8] O. Chapelle and V. Vapnik, “Model selection for Support Vector Machines,” In S. Solla, T. Leen, and K.-R. Müler (Eds.), Adv. Neural Inf. Proc. Syst. 12, Cambridge, MA, MIT Press, 2000.
[9] Steve R. Gunn, “Support Vector Machines for Classification and Regression,” In Technical Report, Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science, 10 May, 1998.
[10] E. Osuna, R. Freund, and F. Girosi, “Support Vector Machines: Training and Applications,” In A.I. Memo 1602, MIT A.I.Lab., 1997.
[11] Johan Suykens, “Least Squares Support Vector Machines,” In Tutorial IJCNN, 2003.
[12] A. J. Smola and B. Schökopf, “On a kernel based method for pattern recognition, regression, approximation and operator inversion,” In Algorithmica, 22:211. 31, 1998, Technical Report 1064, GMD FIRST, April 1997.
[13] C.-F. Lin and S.-D. Wang, “Fuzzy support vector machines,” In IEEE Trans on Neural Networks, Vol.13, No.2, pp. 464-471, 2002.
[14] Y.-M. Huang and S.-X. Du, “Weighted Support Vector Machine for Classification with Uneven Training Class Sizes,” In ICMLC05, 2005.
[15] D. Zhuang, B. Zhang, Q. Yang, J. Yan, Z. Chen, and Y. Chen, “Efficient text classification by weighted proximal SVM,” In Fifth IEEE Int. Conf. on Data Mining, pp. 27-30, Nov. 2005.
[16] B. Li, J. Hu, K. Hirasawa, P. Sun, and K. Marko, “Support Vector Machine with Fuzzy Decision-Making for Real-world Data Classification,” In IEEE World Congress on Computational Intelligence 2006, Int. Joint Conf. on Neural Networks, Canada, 2006.
[17] B. Li, J. Hu, and K. Hirasawa, “Fuzzy Decision-making SVM with An Offset for Real-world Lopsided Data Classification,” In SICEICASE Int. Joint Conf. 2006, Korea, 2006.
[18] A. Asuncion and D. J. Newman, UCI Machine Learning Repository
[http://www.ics.uci.edu/˜mlearn/MLRepository.html],
Irvine, CA, University of California, Department of Information and Computer Science, 2007.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] N. Cristianini and J. Shawe-Taylor, “An Introduction to Support Vector Machines,” Cambridge, UK, Cambridge Univ. Press, 2000.

[2] [2] P. Bartlett and J. Shawe-Taylor, “Generalization performance of support vector machines and other pattern classifiers,” In Advances in Kernel Methods support Vector Learning, Cambridge, MA, MIT Press, 1998.

[3] [3] P. Xu and A. K. Chan, “Support vector machines for multi-class signal classification with unbalanced samples,” In Neural Networks, Proc. of the Int. Joint Conf. on Vol.2, Issue, pp. 20-24, July, 2003.

[4] [4] P. Xu and A. K. Chan, “Support vector machines for multi-class signal classification with unbalanced samples,” In Proc. of the Int. Joint Conf. on Neural Networks, 2003.

[5] [5] T. Eitrich and B. Lang, “Efficient optimization of support vector machine learning parameters for unbalanced datasets,” In Journal of Computational and Applied Mathematics, Vol.196, Issue 2, pp. 425-436, 2006.

[6] [6] S.-X. Du and S.-T. Chen, “Weighted Support Vector Machine for Classification,” In IEEE Int. Conf. on Systems, Man and Cybernetics, 2005.

[7] [7] J. Huang, J. Lin, X. He, and M. Dai, “The Algorithm for Detecting Hiding Information Based on SVM,” In Advances in Neural Networks - ISNN 2004, 2004.

[8] [8] O. Chapelle and V. Vapnik, “Model selection for Support Vector Machines,” In S. Solla, T. Leen, and K.-R. Müler (Eds.), Adv. Neural Inf. Proc. Syst. 12, Cambridge, MA, MIT Press, 2000.

[9] [9] Steve R. Gunn, “Support Vector Machines for Classification and Regression,” In Technical Report, Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science, 10 May, 1998.

[10] [10] E. Osuna, R. Freund, and F. Girosi, “Support Vector Machines: Training and Applications,” In A.I. Memo 1602, MIT A.I.Lab., 1997.

[11] [11] Johan Suykens, “Least Squares Support Vector Machines,” In Tutorial IJCNN, 2003.

[12] [12] A. J. Smola and B. Schökopf, “On a kernel based method for pattern recognition, regression, approximation and operator inversion,” In Algorithmica, 22:211. 31, 1998, Technical Report 1064, GMD FIRST, April 1997.

[13] [13] C.-F. Lin and S.-D. Wang, “Fuzzy support vector machines,” In IEEE Trans on Neural Networks, Vol.13, No.2, pp. 464-471, 2002.

[14] [14] Y.-M. Huang and S.-X. Du, “Weighted Support Vector Machine for Classification with Uneven Training Class Sizes,” In ICMLC05, 2005.

[15] [15] D. Zhuang, B. Zhang, Q. Yang, J. Yan, Z. Chen, and Y. Chen, “Efficient text classification by weighted proximal SVM,” In Fifth IEEE Int. Conf. on Data Mining, pp. 27-30, Nov. 2005.

[16] [16] B. Li, J. Hu, K. Hirasawa, P. Sun, and K. Marko, “Support Vector Machine with Fuzzy Decision-Making for Real-world Data Classification,” In IEEE World Congress on Computational Intelligence 2006, Int. Joint Conf. on Neural Networks, Canada, 2006.

[17] [17] B. Li, J. Hu, and K. Hirasawa, “Fuzzy Decision-making SVM with An Offset for Real-world Lopsided Data Classification,” In SICEICASE Int. Joint Conf. 2006, Korea, 2006.

[18] [18] A. Asuncion and D. J. Newman, UCI Machine Learning Repository
[http://www.ics.uci.edu/˜mlearn/MLRepository.html],
Irvine, CA, University of California, Department of Information and Computer Science, 2007.