A New Fuzzy Membership Assignment Approach for Fuzzy Svm Based on Adaptive Pso in Classification Problems

Volume 14, Issue 2, pp 171-182 http://dx.doi.org/10.22436/jmcs.014.02.08

Download PDF

Download XML

• Export citations
  • CITATIONS & REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • ARTICLE CITATION
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)

• 2666 Downloads
• 3655 Views

Authors

Omid Naghash Almasi - Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran Hamed Sadeghi Gooqeri - Department of Electrical Engineering, Bandar Abbas Branch, Islamic Azad University, Bandar Behnam Soleimanian Asl - Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Mashhad, Iran Wan Mei Tang - Department of Computer and Information Science, Chongqing Normal University, Chongqing

Abstract

Noises will confuse Support Vector Machine (SVM) in the training phase. To overcome this problem, SVM was extended to Fuzzy SVM (FSVM) by incorporating an appropriate fuzzy membership to each data point. Thus, how to choose a proper fuzzy membership is of paramount importance in FSVM. In this paper, Adaptive Particle Swarm Optimization (APSO) method minimizes the generalization error by changing the attributes values of positive and negative class centers to make them free of attribute-noise. As the APSO converged, the fuzzy memberships are assigned for each training data points based on their distance to the corresponding purified class centers with the same class-label. To demonstrate the effectiveness of the proposed FSVM, its performance on artificial and real-world data sets is compared with three FSVM algorithms in the literature.

Share and Cite

Keywords

• Fuzzy support vector machine
• Fuzzy membership function
• Adaptive particle swarm optimization
• Attribute-noise.

MSC

•  62H86
•  68T05
•  93C42

References

• [1] V. N. Vapnik, Statistical learning theory, New York: Wiley, (1998)
  • Google Scholar


• [2] I. Guyon, B. Boser, V. Vapnik , Automatic capacity tuning of very large VC-dimension classifier, Adv. Neural Inform. Process. Systems, 5 (1993), 147–155.
  • Google Scholar


• [3] V. N. Vapnik, An overview of statistical learning theory, IEEE Transaction on Neural Networks, 10(5) (1999), 988–999.
  • View Article
  • Google Scholar


• [4] C. J. C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining Knowl. Discovery, 2(2) (1998), 955–974.
  • Google Scholar


• [5] X. G. Zhang, Using class-center vectors to build support vector machines, In Proc. IEEE Signal Process. Soc. Workshop. NewYork: IEEE Press, (1999), 3–11.
  • View Article
  • Google Scholar


• [6] H. Xiong, G. Pandey, M. Steinbach, V. Kumar, Enhancing Data Analysis with Noise Removal, IEEE Transaction on Knowledge and Data Engineering, 18(3) (2006), 304–319.
  • View Article
  • Google Scholar


• [7] J. V. Hulse, T. Khoshgoftaar, Knowledge discovery from imbalanced and noisy data, Data & Knowledge Engineering, 68 (2009), 1513–1542.
  • View Article
  • Google Scholar


• [8] M. E. Mavroforakis, S. Theodoridis, A Geometric Approach to Support Vector Machine (SVM) Classification, IEEE Transaction on Neural Networks, 17(3) (2006), 671–682.
  • View Article
  • Google Scholar


• [9] A. Angelova, Y. Abu-Mostafa, P. Perona, Pruning training sets for learning of object categories, IEEE Conference on Computer Vision and Pattern Recognition, (2005), 494–501.
  • View Article
  • Google Scholar


• [10] X. Zhu, X. Wu, Class noise vs. attribute noise: a quantitative study of their impacts, Artificial Intelligence Review, 22 (3–4) (2004), 177–210.
  • View Article
  • Google Scholar
  • MATH


• [11] C. E. Brodley, M. A. Friedl, Identifying mislabeled training data, Journal of Artificial Intelligence Research, 11 (1999), 131–167.
  • View Article
  • Google Scholar
  • MATH


• [12] J. V. Hulse, T. M. Khoshgoftaar, H. Huang, The pairwise attribute noise detection algorithm, Knowledge and Information Systems Journal, Special Issue on Mining Low Quality Data., 11(2) (2007), 171–190.
  • View Article
  • Google Scholar


• [13] T. M. Khoshgoftaar, S. Zhong, V. Joshi, Enhancing software quality estimation using ensemble-classifier based noise filtering, Intelligent Data Analysis: An International Journal. , 9(1) (2005), 3–27.
  • Google Scholar


• [14] X. Wu, X. Zhu, Mining With Noise Knowledge: Error-Aware Data Mining, IEEE Transaction on Man, and Cybernetics—PART A: Systems and Humans, 38(4) (2008), 917–932.
  • View Article
  • Google Scholar


• [15] C. F. Lin, S. D. Wang, Fuzzy support vector machines, IEEE Transaction on Neural Networks., 13(2) (2002), 464–471.
  • View Article
  • Google Scholar


• [16] C. F. Lin, S. D. Wang, Training algorithms for fuzzy support vector machines with noisy data, Pattern Recognition Letters., 25(14) (2004), 1647–1656.
  • View Article
  • Google Scholar


• [17] X. F. Jiang, Y. Zhang, J. C. Lv, Fuzzy SVM with a new fuzzy membership function, Neural Comput. Appl., 15 (2006), 268–276.
  • View Article
  • Google Scholar


• [18] W. M. Tang, Fuzzy SVM with a new fuzzy membership function to solve the two-class problems, Neural Processing Letters., 34(3) (2011), 209–219.
  • View Article
  • Google Scholar


• [19] K. M. Lin, C. J. Lin, A study on reduced support vector machines, IEEE Transactions on Neural Networks, 14(6) (2003), 1449–1459.
  • View Article
  • Google Scholar


• [20] A. Bordes, S. Ertekin, J. Weston, L. Bottou, Fast kernel classifiers with online and active learning, The Journal of Machine Learning Research., 6 (2005), 1579–1619.
  • MathSciNet
  • Google Scholar
  • MATH


• [21] J. Weston, Leave-one-out support vector machines, In: Dean, T. (Ed.), Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 99.Morgan Kaufmann, (1999), 727–733.
  • Google Scholar


• [22] M. W. Browne, Cross-validation methods, Journal of Mathematical Psychology., 44(1) (2000), 108–132.
  • View Article
  • Google Scholar


• [23] D. C. Li, Y. H. Fang, Y. M. Fang, The data complexity index to construct an efficient cross-validation method, Decision Support Systems. , 50(1) (2010), 93–102.
  • View Article
  • Google Scholar


• [24] J. Kennedy, R. C. Eberhart, Swarm Intelligence, USA: Academic Press, (2001)
  • Google Scholar


• [25] Y. H. Shi, R. C. Eberhart, A modified particle swarm optimizer, IEEE World congress on computer intelligence., (1998), 69–73.
  • View Article
  • Google Scholar


• [26] A. Nickabadi, M. M. Ebadzadeh, R. Safabakhsh, A novel particle swarm optimization algorithm with adaptive inertia weight, Applied Soft Computing. , 11(4) (2011), 3658–3670.
  • View Article
  • Google Scholar


• [27] K. T. Chaturvedi, M. Pandit, L. Srivastava, Self-organizing hierarchical particle swarm optimization for non-convex economic dispatch, IEEE Transaction on Power Systems, 23 (3) (2008), 1079–1087.
  • View Article
  • Google Scholar


• [28] A. Ratnaweera, S. K. Halgamuge, H. C. Watson, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transaction on Evol. Comput. , 8(3) (2004), 240–255.
  • View Article
  • Google Scholar


• [29] S. S. Keerthi, C. J. Lin, Asymptotic behavior of support vector machines with gaussian kernel, Neural Computation. , 15(7) (2003), 1667–1689.
  • View Article
  • Google Scholar
  • MATH


• [30] H. T. Lin, C. J. Lin, A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO–type methods, Technical report, Department of computer science national Taiwan university., (2003), 1–32.
  • Google Scholar


• [31] S. Li, M. Tan, Tuning SVM parameters by using a hybrid CLPSO–BFGS algorithm, Neurocomputing, 73(10) (2010), 2089–2096.
  • View Article
  • Google Scholar


• [32] C. L. Blake, C. J. Merz, UCI Repository of Machine Learning Databases, Tech. Rep. Univ. California, Dept, Information and Computer Science, Irvine, CA (1998)
  • Google Scholar