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Cluster Computing

, Volume 22, Supplement 5, pp 11965–11974 | Cite as

Identification of suitable membership and kernel function for FCM based FSVM classifier model

  • P. SrideviEmail author
Article
  • 96 Downloads

Abstract

Support vector machine (SVM) a new machine learning algorithm emerging as an effective classification technique than neural networks and other statistical classifiers. Its performance is however, observed to be affected by presence of outliers or noise in the dataset. So, to enhance the generalization capability of SVM, researchers focussed on developing fuzzy support vector machines (FSVM), which works on the concept of fuzzy membership to data points for learning the best decision hyperplane to classify data points. In such case, the choice of membership for data points is critical for good behaviour of the model. So, this paper focuses on finding right membership using optimal fuzzy index parameter (m) and demonstrating its significance in membership and classifier performance. This research work focussed on finding membership values using FCM and final membership from derived low value and high value membership function and incorporating them in FSVM model, to find the best membership value from analysis of five datasets used in the study. For linear classification of data, as kernel functions can show its influence, the study also performed analysis on selection of appropriate kernel function that can best fit the FCM based FSVM classifier model and prove enhancement of generalization performance with our research findings. High and low membership functions are compared through computational analyses and the membership that results in better generalization capability in the model is proven in the study.

Keywords

Fuzzy support vector machine (FSVM) Fuzzy c-means (FCM) Fuzzy index Optimality Generalization performance Membership 

References

  1. 1.
    Almasi, O.N., Gooqeri, H.S., Asl, B.S., Tang, W.M.: A new fuzzy membership assignment approach for fuzzy SVM based on adaptive PSO in classification problems. J. Math. Comput. Sci. 14, 171–182 (2015)CrossRefGoogle Scholar
  2. 2.
    Beynon, M.J., Peel, M.J.: Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega 29(6), 561–576 (2001)CrossRefGoogle Scholar
  3. 3.
    Bezdeck, J.C., Ehrlich, R., Full, W.: FCM: fuzzy C-means algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)CrossRefGoogle Scholar
  4. 4.
    Chen, C.F., Lee, J.M.: The validity measurement of fuzzy c-means classifier for remotely sensed images. In: Paper Presented at the 22nd Asian Conference on Remote Sensing, pp. 9 (2001)Google Scholar
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  6. 6.
    Herbrich, R., Jason W.: Adaptive margin support vector machines for classification. In: Paper Presented at the 9th International Conference on Artificial Neural Networks, pp. 880–885 (1999)Google Scholar
  7. 7.
    Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., Wu, S.: Credit rating analysis with support vector machines and neural networks: a market comparative study. Decis. Support Syst. 37(4), 543–558 (2004)CrossRefGoogle Scholar
  8. 8.
    Hussain et el.: A novel hybrid fuzzy-SVM image steganographic model. In: IEEE International Symposium on Information Technology (ITSim), vol. 1, pp. 1–6 (2010)Google Scholar
  9. 9.
    Jiang, X., Yi, Z., Lv, J.C.: Fuzzy SVM with a new fuzzy membership function. Neural Comput. Appl. 15(3–4), 268–276 (2006)CrossRefGoogle Scholar
  10. 10.
    Ju, W., Shan, J., Yan, C., Cheng, H.D.: Discrimination of disease-related non- synonymous single nucleotide polymorphisms using multi-scale GAUSSIAN -RBF kernel fuzzy support vector machine. Pattern Recogn. Lett. 30(4), 391–396 (2009)CrossRefGoogle Scholar
  11. 11.
    Li, Y.B., Li, Y: Survey on uncertainty support vector machine and its application in fault diagnosis. In: 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 561–565 (2010)Google Scholar
  12. 12.
    Li, J., Yu, Z.: An improved adaptive support vector machine algorithm with combinational fuzzy C-means clustering. In: 2nd International Conference on Advanced Computer Control (ICACC), pp. 269–272 (2010)Google Scholar
  13. 13.
    Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Trans. Neural Netw. 13(2), 464–471 (2002)CrossRefGoogle Scholar
  14. 14.
    Lin, C.F., Wang, S.D.: Training algorithms for fuzzy support vector machines with noisy data. Pattern Recogn. Lett. 25(14), 1647–1656 (2004)CrossRefGoogle Scholar
  15. 15.
    Markowetz, F.: Support vector machines in bioinformatics. Master’s thesis, University of Heidelberg (2001)Google Scholar
  16. 16.
    Öğüt, H., Mete Doğanay, M., Aktaş, R.: Detecting stock-price manipulation in an emerging market, the case of Turkey. Expert Syst. Appl. 36(9), 11944–11949 (2009)CrossRefGoogle Scholar
  17. 17.
    Perez, M., Rubin, D. M., Scott, L.E., Marwala, T., Stevens, W.: A hybrid fuzzy-svm classifier, applied to gene expression profiling for automated leukemia diagnosis. In: IEEE 25th Convention on Electrical and Electronics Engineers in Israel, pp. 41–45 (2008)Google Scholar
  18. 18.
    Ramze Rezaee, M., Lelieveldt, B.P., Reiber, J.H.: A new cluster validity index for the fuzzy c-mean. Pattern Recogn. Lett. 19(3), 237–246 (1998)zbMATHCrossRefGoogle Scholar
  19. 19.
    Shilton, A., Lai, D.T.: Iterative fuzzy support vector machine classification. In: IEEE Proceedings of Fuzzy Systems Conference, FUZZ-IEEE, pp. 1–6 (2007)Google Scholar
  20. 20.
    Soman, K., Loganathan, R., Ajay, V.: Machine Learning with SVM and Other Kernel Methods. PHI Learning Pvt. Ltd, New Delhi (2009)Google Scholar
  21. 21.
    Song, Q., Hu, W., Xie, W.: Robust support vector machine with bullet hole image classification. IEEE Trans. Syst. Man Cybern. C 32(4), 440–448 (2002)CrossRefGoogle Scholar
  22. 22.
    Tang, Y., Sun, F., Sun, Z.: Improved validation index for fuzzy clustering. In: IEEE Conference on Proceedings of the American Control, pp. 1120–1125 (2005)Google Scholar
  23. 23.
    Tao, Q., Wang, J.: A new fuzzy support vector machine based on the weighted margin. Neural Process. Lett. 20(3), 139–150 (2004)CrossRefGoogle Scholar
  24. 24.
    Wang, T.Y., Chiang, H.M.: Fuzzy support vector machine for multi-class text categorization. Inf. Process. Manag. 43(4), 914–929 (2007)CrossRefGoogle Scholar
  25. 25.
    Wang, Y., Wang, S., Lai, K.K.: A new fuzzy support vector machine to evaluate credit risk. IEEE Trans. Fuzzy Syst. 13(6), 820–831 (2005)CrossRefGoogle Scholar
  26. 26.
    Wu, K.L.: Analysis of parameter selections for fuzzy c-means. Pattern Recogn. 45(1), 407–415 (2012)zbMATHCrossRefGoogle Scholar
  27. 27.
    Wu, Z., Zhang, H., Liu, J.: A fuzzy support vector machine algorithm for classification based on a novel PIM fuzzy clustering method. Neurocomputing 12(5), 119–124 (2014)CrossRefGoogle Scholar
  28. 28.
    Xiao, J., Tong, Y.: Research of Brain MRI image segmentation algorithm based on FCM and SVM. In: Paper Presented at the Control and Decision Conference 2014, China (2014)Google Scholar
  29. 29.
    Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Machine Intell. 13(8), 841–847 (1991)CrossRefGoogle Scholar
  30. 30.
    Xiong, S.W., Liu, H.B., Niu, X.X.: Fuzzy support vector machines based on FCM clustering. In: IEEE Proceedings of International Conference on Machine Learning and Cybernetics, pp. 2608–2613 (2005)Google Scholar
  31. 31.
    Yang, X., Song, Q., Wang, Y.: A weighted support vector machine for data classification. Int. J. Pattern Recognit. Artif. Intell. 21(5), 961–976 (2007)CrossRefGoogle Scholar
  32. 32.
    Yang, X., Zhang, G., Lu, J., Ma, J.: A kernel fuzzy c-means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises. IEEE Trans. Fuzzy Syst. 19(1), 105–115 (2011)CrossRefGoogle Scholar
  33. 33.
    Yeh, C.Y., Su, W.P., Lee, S.J.: Employing multiple-kernel support vector machines for counterfeit banknote recognition. Appl. Soft Comput. 11(1), 1439–1447 (2011)CrossRefGoogle Scholar
  34. 34.
    Zhang, X,G.: Using class-center vectors to build support vector machines. In: Proceedings of IEEE Signal Process Soc.Workshop. New York, IEEE Press, pp. 3–11 (1999)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Management StudiesNational Institute of TechnologyTiruchirappalliIndia

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