Multi-Objective Particle Swarm Classifier
Subject Areas : electrical and computer engineering
Seyed-Hamid
Zahiri
1
(University of Birjand)
Keywords: Multi-objective particle swarm optimizationpattern recognitionclassifierswarm intelligence,
Abstract :
A multi-objective particle swarm optimization (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed classifier can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not have any over-fitting and over-learning problems. Other swarm intelligence based classifiers do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem. The experimental results show that the proposed multi-objective classifier can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices.
[1] S. H. Zahiri and S. A. Seyedin, "Particle swarm classifiers," in Proc. of the 13th Iranian Conf. of Elec. Eng., ICEE 2005, pp. 454-458, Zanjan, Iran, 2005.
[2] S. H. Zahiri and S. A. Seyedin, "Intelligent particle swarm classifier," Iranian J. of Electrical and Computer Engineering, vol.4, no. 5, Winter-Spring, 2005.
[3] S. H. Zahiri and S. A. Seyedin, "Swarm intelligence based classifiers," Accepted for Publication by the J. of the Franklin Institute, Available online at Elsevier scincedirect from May 2006.
[4] C. A. C. Coello and M. S. Lechuga, "MOPSO: a proposal for multiple objective particle swarm optimization," in Proc of the 2002 Congress on Evolutionary Computation, CEC '02, vol. 2, pp. 1051-1056, May 2002.
[5] S. L. Ho, S. Yang, N. Guangzheng, E. W. C. Lo, and H. C. Wong, "A particle swarm optimization-based method for multiobjective design optimizations," IEEE Trans. on Magnetic, vol. 41, no. 5, pp. 1756-1759, May 2005.
[6] C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE Trans. on Evolutionary Computation, vol. 8, no. 3, pp. 256-279, Jun. 2004.
[7] J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proc. IEEE Intl. Conf. on Neural Networks IV, pp. 1942-1948, 1995.
[8] Y. Shi and R. C. Eberhart, "Empirical study of particle swarm optimization," in Proc. of the 1999 Cong. on Evolutionary Computation, pp. 1945-1950, 1999.
[9] R. C. Eberhart and Y. Shi, "Tracking and optimizing dynamic systems with particle swarms," in Proc. of the 2001 Cong. on Evolutionary Computation, vol. 1, pp. 94-100, 2001.
[10] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles, Addison-Wesely, Reading MA, 1974.
[11] R. A. Fisher, "The use of multiple measurements in taxonomic problems," Ann. Eugen, vol. 7, pp. 179-188, 1936.
[12] , UCI Machine Learning Databases, University of California, Irvine, via anonymous ftp:ftp.ics.uci.edu/pub/machine-learning-databases.
[13] D. J. Strausberger, F. D. Garber, N. F. Chamberlain, and E. K. Walton, "Modeling and performance of HF/OTH radar target classification systems," IEEE Trans. on Aerospace and Electronic Systems, vol. 28, no. 2, pp. 396-402, Apr. 1992.
[14] M. A. Morgan, "Target I. D. using natural resonance, a new concept for future radar systems," IEEE Potential, pp. 11-14, Dec. 1993.
[15] N. F. Chamberlain, E. K. Walton, and F. D. Garber, "Radar target identification of aircraft using polarization-diverse features," IEEE Trans. on Aerospace and Electronic Systems, vol. 27, no. 1, pp. 58-66, Jan. 1991.
[16] M. R. Bell and R. A. Grubbs, "JEM modeling and measurement for radar target identification," IEEE Trans. on Aerospace and Electronic Systems, vol. 29, no. 1, pp. 73-87, Jan. 1993.
[17] J. Martin and B. Mulgrew, "Analysis of the theoretical radar returned signal from aircraft propeller blades," in Proc. of the IEEE Int. Radar Conf., pp. 569-572, 1990.