Browse > Article
http://dx.doi.org/10.5370/KIEE.2018.67.8.1071

Design of SVM-Based Polynomial Neural Networks Classifier Using Particle Swarm Optimization  

Roh, Seok-Beom (Dept. of Electrical and Electronic Engineering, University of Suwon)
Oh, Sung-Kwun (Dept. of Electrical and Electronic Engineering, University of Suwon)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.67, no.8, 2018 , pp. 1071-1079 More about this Journal
Abstract
In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.
Keywords
Support vector machine; Polynomial neural networks; Dynamically generated networks; Support vector machine based polynomial neural networks; Multi-objective particle swarm optimization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 G. B. Hwang, Q. U. Zhu, C. K. Siew, "Extreme Learning Machine: Thoery and Applicaitons", Neurocomputing, vol. 70, pp. 489-501, 2006   DOI
2 S.-K. Oh and Pedryza, W, "The Design of Self- Organizing Polynomial Neural Networks", Information Science, vol. 141, pp. 237-258, 2002   DOI
3 S.-K. Oh and W. Pedrycz, "Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems", Fuzzy sets and Systems, vol. 115, no. 2, pp. 205-230, 2000.   DOI
4 S.-K. Oh, W. Pedrycz, and D.-W. Kim, "Hybrid Fuzzy Polynomial Neural Networks", Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 3, pp. 257-280, June, 2002.   DOI
5 S.-K. Oh and W. Pedrycz, "The design of self-organizing Polynomial Neural Networks", Information Science, vol. 141, pp. 237-258, 2002.   DOI
6 S.-K. Oh, W. Pedrycz and B.-J. Park, "Polynomial Neural Networks Architecture: Analysis and Design", Computers and Electrical Engineering, vol. 29, Issue 6, pp. 703-725, 2003.   DOI
7 C. Cortes and V. N. Vapnik, "Support-vector networks", Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995   DOI
8 T. B. Trafalis and H. Ince, "Support vector machine for regression and applications to financial forecasting", in Proc. IEEE-INNSENNS Int. Joint Conf. Neural Netw., vol. 6, pp. 348-353, 2000
9 W. S. Noble, "Support vector machine application in computational biology", in Kernel Methods in Computational Biology, B Schokopf, K. Tsuda, and J.-P. Vert, Eds. Cambridge, MA, USA: MIT Press, 2004
10 K.-S. Goh, E.-Y. Chang, and B.-T. Li, "Using one-class and two-class SVMs for multiclass image annotation", IEEE Trans. Knowl. Data Eng., vol. 17, no. 10, pp. 1333-1346, 2005   DOI
11 D. Isa, L.-H. Lee, V. P. Kallimani, and R. RajKumar, "Text document preprocessing with the Bayes formula for classification using the support vector machine", IEEE Trans.Knowl. Data Eng., vol. 20, no. 9, pp. 1264-1272, 2008   DOI
12 R. E. Perez and K. Behdinan, "Particle swarm approach for structural design optimization", Computer & Structures, vol. 85, Issues 19-20, pp. 1579-1588, 2007   DOI
13 M. B. Karstan, "Kernel methods in bioinformatics", in Handbook of Statistical Bioinformatics, Part 3, New York, NY, USA: Springer, pp. 317-334, 2011
14 C. A. Coello Coello, and M. S. Lechuga, "MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization," Proceedings of the 2002 IEEE Congress on Evolutionary Computation, pp. 12-17.
15 E. Frank, M. A. Hall, and I. H. Witten, The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.