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Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine

by M. H. Marghny, Rasha M. Abd El-aziz, Ahmed I. Taloba
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 19
Year of Publication: 2014
Authors: M. H. Marghny, Rasha M. Abd El-aziz, Ahmed I. Taloba
10.5120/19023-0540

M. H. Marghny, Rasha M. Abd El-aziz, Ahmed I. Taloba . Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine. International Journal of Computer Applications. 108, 19 ( December 2014), 38-46. DOI=10.5120/19023-0540

@article{ 10.5120/19023-0540,
author = { M. H. Marghny, Rasha M. Abd El-aziz, Ahmed I. Taloba },
title = { Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 19 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number19/19023-0540/ },
doi = { 10.5120/19023-0540 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:43:26.281201+05:30
%A M. H. Marghny
%A Rasha M. Abd El-aziz
%A Ahmed I. Taloba
%T Differential Search Algorithm-based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 19
%P 38-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which affects the performance of SVM classi?er. Recently, the Generalized Eigenvalue Proximal SVM (GEPSVM) has been presented to solve the SVM complexity. In real world applications data may affected by error or noise, working with this data is a challenging problem. In this paper, an approach has been proposed to overcome this problem. This method is called DSA-GEPSVM. The main improvements are carried out based on the following: 1) a novel fuzzy values in the linear case. 2) A new Kernel function in the nonlinear case. 3) Differential Search Algorithm (DSA) is reformulated to ?nd near optimal values of the GEPSVM parameters and its kernel parameters. The experimental results show that the proposed approach is able to find the suitable parameter values, and has higher classification accuracy compared with some other algorithms.

References
  1. M. H. Marghny, Rasha M. Abd El-Aziz, and Ahmed I. Taloba. An Effective Evolutionary Clustering Algorithm: Hepatitis C case study. International Journal of Computer Applications, 2011, 34(6): 1-6.
  2. M. H. Marghny, and Ahmed I. Taloba. Outlier Detection using Improved Genetic K-means. International Journal of Computer Applications, 2011, 28(11): 33-36.
  3. Adel A. Sewisy, M. H. Marghny, Rasha M. Abd El-Aziz, and Ahmed I. Taloba. Fast Efficient Clustering Algorithm for Balanced Data. International Journal of Advanced Computer Science and Applications (IJACSA), 2014, 5(6): 123-129.
  4. C. C. Aggarwal, and C. K Reddy. Data clustering: algorithms and applications. Chapman and Hall/CRC Press, 2013.
  5. M. H. Marghny, and I. E. El-Semman. Extracting logical classification rules with gene expression programming: microarray case study. Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AIML 05), Cairo, Egypt, 2005, 11–16.
  6. M. H. Marghny, and I. E. El-Semman. Extracting fuzzy classification rules with gene expression programming. . Proceedings of the International Conference on Artificial Intelligence and Machine Learning (AIML 05), Cairo, Egypt, 2005.
  7. M. H. Marghny, and H. E. Refaat. A new parallel association rule mining algorithm on distributed shared memory system. International Journal of Business Intelligence and Data Mining, 2012, 7(4): 233-252.
  8. M. H. Marghny, and A. A. Shakour. Fast, Simple and Memory Efficient Algorithm for Mining Association Rules. International Review on Computers & Software, 2007, 2(1).
  9. M. H. Marghny, and A. A. Shakour. Scalable Algorithm for Mining Association Rules. ICCST, 2006, 6(3): 55-60.
  10. C. Cortes, and V. N. Vapnik. Support vector networks. Machine Learning, 1995, 20(3): 273 – 297.
  11. Glenn Fung, and Olvi L. Mangasarian. Proximal support vector machine classi?ers. In Proceedings of the Seventh ACM SIGKDD International conference on knowledge discovery and data mining, KDD 01, ACM, New York, NY, USA, 2001, 77–86.
  12. Olvi L. Mangasarian, and Edward W. Wild. Multisurface proximal support vector machine classi?cation via generalized eigenvalues. IEEE transaction on pattern analysis and machine intelligence, 2006, 28(1): 69–74.
  13. Abe S, and Inoue T. Fuzzy Support Vector Machines for Pattern Classification. IEEE, 2001, 2: 1449-1454.
  14. Chun-Fu Lin, and Sheng-De Wang. Fuzzy support vector machines. IEEE transactions on neural networks, 2002, 13(2): 464-471.
  15. Jayadeva, R. Khemchandani, and S. Chandra. Fuzzy proximal support vector classi?cation via generalized eigenvalues. Springer-Verlag Berlin Heidelberg, 2005, 360 –363.
  16. Jayadeva, R. Khemchandani, and S. Chandra. Fuzzy multi-category proximal support vector classi?cation via generalized eigenvalues. Soft Computing – A Fusion of Foundations, Methodologies and Applications, 2007, 11: 679–685.
  17. Ding Shifei, and Gu Yaxiang. A fuzzy support vector machine algorithm with dual membership based on hypersphere, Journal of computational information systems, 2011, 7(6): 2028-2034.
  18. Li. Kai, and Hongyan Ma. A fuzzy twin support vector machine algorithm. International journal of application or innovation in engineering & management, 2013, 2(3): 459- 465.
  19. M. R. Guarracino, A. Irpino, R. Jasinevicius, and R. Verde. Fuzzy regularized generalized eigenvalue classi?er with a novel membership function. Information Sciences, 2013, 245: 53–62.
  20. Huang. Cheng-Lung, and Chieh-Jen Wang. A ga-based feature selection and parameters optimization for support vector machines. Expert systems with applications, 2006, 31: 231–240.
  21. S. Lin, Z. Lee, S. Chen, and T. Tseng. Parameter determination of support vector machine and feature selection using simulated annealing approach. Applied soft computing, 2008, 8(4): 1505–1512.
  22. S. Lin, K. Ying, S. Chen, and Z. Lee. Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert systems with applications, 2008, 35(4): 1817–1824.
  23. L. Luo, D. Huang, H. Peng, Q. Zhou, G. Shao, and F. Yang. A new parameter selection method for support vector machine based on the decision value. Convergence information technology, 2010, 5(8): 36–41.
  24. S. C. Chen, S. W. Linb, and S. Y. Chou. Enhancing the classi?cation accuracy by scatter-based ensemble approach. Applied soft computing, 2011, 11(1): 1021–1028.
  25. X. Zhang, D. Qiu, and F. Chen. Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis. Neurocomputing, 2014, 149(Part B): 641–651.
  26. P. Civicioglu. Understanding the nature of evolutionary search algorithms. Additional technical report for the project of 110Y309-Tubitak, 2013.
  27. D. Goswami, and S. Chakraborty. Differential search algorithm-based parametric optimization of electrochemical micromachining processes. International journal of industrial engineering computations, 2014, 5(1): 41–54.
  28. P. Civicioglu. Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 2012, 46: 229-247.
  29. X. Song, L. Li, X. Zhang, X. Shi, J. Huang, J. Cai, S. Jin, J. Ding. An implementation of differential search algorithm (DSA) for inversion of surface wave data. Journal of Applied Geophysics, 2014, 111: 334-345.
  30. R. Devi, E. Barlaskar, O. Devi, S. Medhi, and R. Shimray. Survey on evolutionary computation tech techniques and its application in different fields. International Journal on Information Theory (IJIT), 2014, 3(3): 73-82.
  31. Y. Amrane, M. Boudour, and M. Belazzoug. A new optimal reactive power planning based on Differential Search Algorithm. International Journal of Electrical Power & Energy Systems, 2015, 64: 551-561.
  32. M. R. Guarracino, C. Cifarelli, O. Seref, and PM. Pardalos. A classi?cation algorithm based on generalized eigenvalue problems. Optimization methods and software, 2007, 22(1): 73–81.
  33. M. R. Guarracino, A. Irpino, and R. Verde. Multiclass generalized eigenvalue proximal support vector machines. Complex, Intelligent and Software Intensive Systems , IEEE Computer Society, 2010, 25–32.
  34. Y. Shao, N. Deng, W. Chen, and W. Zhen. Improved generalized eigenvalue proximal support vector machine. IEEE signal processing letters, 2013, 20(3): 213-216.
  35. P. Xanthopoulos, M. R. Guarracino, and P. M. Pardalos. Robust generalized eigenvalue classi?er with ellipsoidal uncertainty. Ann Oper Res, 2014, 216(1): 327–342.
  36. A. N. Tikhonov, and V. Y. Arsenin. Solutions of Ill posed problems. New York: john wiley and sons, 1977.
  37. B. N. Parlett. The symmetric eigenvalue problem. Philadelphia: SIAM, 1998.
  38. A. Afifi. Improving the classification accuracy using support vector machines (SVMS) with new kernel. Journal of global research in computer science, 2013, 4(2): 1-7.
  39. K. Bache, and M. Lichman. UCI Machine Learning Repository [http://archive. ics. uci. edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machines Generalized Eigenvalues Proximal Classifier Fuzzy Data Classification Differential Search Algorithm Kernel Function.

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