Using Swarm Intelligence for Improving Accuracy of Fuzzy Classifiers
This paper discusses a method for improving accuracy
of fuzzy-rule-based classifiers using particle swarm optimization
(PSO). Two different fuzzy classifiers are considered and optimized.
The first classifier is based on Mamdani fuzzy inference system
(M_PSO fuzzy classifier). The second classifier is based on Takagi-
Sugeno fuzzy inference system (TS_PSO fuzzy classifier). The
parameters of the proposed fuzzy classifiers including premise
(antecedent) parameters, consequent parameters and structure of
fuzzy rules are optimized using PSO. Experimental results show that
higher classification accuracy can be obtained with a lower number
of fuzzy rules by using the proposed PSO fuzzy classifiers. The
performances of M_PSO and TS_PSO fuzzy classifiers are compared
to other fuzzy based classifiers
[1] Yager, R. R. and Filev, D. P. "Essentials of Fuzzy Modeling and
Control," John Wiley, New York, U.S.A. (1994).
[2] Jang, J. S., Sun, C. T. and Mizutani, E., " Neuro-Fuzzy and Soft
Computing," Prentice Hall, New Jersey, U.S.A. (1997).
[3] J.C. Bezdek, "Pattern Recognition with Fuzzy Objective Function
Algorithm," Plenum Press, New York, 1981.
[4] J.C. Bezdek, "On the relationship between neural networks, pattern
recognition and intelligence," Int. J. Approx. Reasoning 6 (2) (1992) 85-
107.
[5] C.T. Lin, C.S.G. Lee, "Neural network-based fuzzy logic control and
decision system," IEEE Trans. Comput. 40 (12) (1991) 1320-1336.
[6] C.F. Juang, J.Y. Lin, C.T. Lin, "Genetic reinforcement learning through
symbiotic evolution for fuzzy controller design," IEEE Trans. Syst. Man
Cybern. Part B 30 (2) (2000) 290-302.
[7] C.L. Karr, "Design of an adaptive fuzzy logic controller using a genetic
algorithm," Proceedings of the Fourth International Conference on
Genetic Algorithm, San Diego, July 12-16, 1991, pp. 450-457.
[8] C.L. Karr, E.J. Gentry, "Fuzzy control of pH using genetic algorithm,"
IEEE Trans. Fuzzy Systems. 1 (1) (1993) 46-53.
[9] M. Setnes, H. Roubos, "GA-Fuzzy modeling and classification:
complexity and performance," IEEE Trans. Fuzzy Systems. 8 (4) (2000)
509-522.
[10] Mahamed G.H. Omran, Andries P Engelbrecht, and Ayed Salman,
"Dynamic Clustering using Particle Swarm Optimization with
Application in Unsupervised Image Classification," Proceeding of world
academy of science. Engineering and technology, volume 9, November
2005 ISSN 1307-6884.
[11] Chia-Chong Chen "Design of PSO-based Fuzzy Classification Systems",
Tamkang Journal of Science and Engineering," Vol. 9, No 1, (2006) pp.
63_70
[12] Ishibuchi, H., Nozaki, K., Yamamoto, N. and Tanaka, H., "Selecting
Fuzzy If-Then Rules for Classification Problems Using Genetic
Algorithms," IEEE Trans. Fuzzy Systems, Vol. 3, (1995) pp. 260_270.
[13] Jang, J. S., "ANFIS: Adaptive-Network-Based Fuzzy Inference
systems," IEEE Trans. on Systems, Man and Cybernetics, Vol. 23,
(1993) pp. 665_685.
[14] Nozaki, K., Ishibuchi, H. and Tanaka, H., "Adaptive Fuzzy Rule-Based
Classification Systems," IEEE Trans. on Fuzzy Systems, Vol. 4, No. 3,
Aug., (1996) pp. 238_250.
[15] Wang, L. X. and Mendel, J. M., "Generating Fuzzy Rules by Learning
from Examples," IEEE Trans. On Systems, Man and Cybernetics, Vol.
22, (1992) pp. 1414_ 1427.
[16] Wong, C. C. and Chen, C. C., "A GA-Based Method for Constructing
Fuzzy Systems Directly from Numerical Data," IEEE Trans. on Systems,
Man and Cybernetics- Part B: Cybernetics, Vol. 30, (2000) pp.
904_911.
[17] Simpson, P. K., "Fuzzy Min-Max Neural Networks- Part 1:
Classification," IEEE Trans. Neural Networks, Vol. 3, Sep., (1992) pp.
776_786.
[18] Wong, C. C. and Chen, C. C., "A Hybrid Clustering and Gradient
Descent Approach for Fuzzy Modeling," IEEE Trans. on Systems, Man
and Cybernetics-Part B: Cybernetics, Vol. 29, (1999) pp. 686_693.
[19] Xie, X.L., and G.Beni, "A validity measure for fuzzy clustering," IEEE
trans. Pattern Anal. Mach. Intell. 3(8): 1991 pp. 841-846.
[20] S. L. Chiu, "Fuzzy Model Identification Based on Cluster Estimation,"
Journal of Intelligent and Fuzzy Systems, 1994 pp. 267-278,.
[21] Emami, M.R. Turksen, Burhan. Goldenberg A.A.,. "Development of A
Systematic Methodology of Fuzzy Logic Modeling," IEEE transaction
on Fuzzy Systems, 6(3) 1998.
[22] Salehfar, H. et al., "A systematic approach to linguistic fuzzy modeling
based on input-output data," IEEE Proceedings of the 2000 Winter
Simulation Conference, 2000.
[23] Vachkov, G. and Fukuda, T. , "Multilevel composite fuzzy models," The
Ninth IEEE International Conference on Fuzzy Systems Volume 1, Issue
, 2000 pp. 453 - 458.
[24] Hwang, H.S., "Automatic design of fuzzy rule base for modeling and
control using evolutionary programming," IEE Proc-Control Theory
Appl, Vol 146, 1999.
[25] A. Homaifar, E. McCormick, "Simultaneous design of membership
functions and rule sets for fuzzy controller using genetic algorithms,"
IEEE Trans. Fuzzy Systems. 3 (2) (1995) 129-139.
[26] O. Cordon, F. Herrera, P. Villar, "Generating the knowledge base of a
fuzzy rule-based system by the genetic learning of the data base," IEEE
Trans. Fuzzy Systems. 9 (4) (2001) 667-674.
[27] S.Y. Ho, H.M. Chen, S.J. Ho, T.K. Chen, "Design of accurate classifiers
with a compact fuzzy-rule base using an evolutionary scatter partition of
feature space," IEEE Trans. Syst. Man Cybern. Part B 34 (2) (2004)
1031-1044.
[28] Enwang Zhou, Alireza Khotanzad, "Fuzzy classifier design using genetic
algorithms", Pattern Recognition 40 (2007) 3401 - 3414.
[29] Tanya Mirzayans, Nitin Parimi, Patrick Pilarski, Chris Backhouse,Loren
Wyard-Scott, Petr Musilek , "A Swarm Based System for Object
Recognition," Neural Network World 3/05, 2005, pp. 243-255.
[30] Ata E. Zadeh Shermeh and Reza Ghaderi, "An Intelligent System for
Classification of the Communication Formats Using PSO," Informatica
32 (2008) pp. 213-218.
[31] Mamdani, E.H. and S. Assilian, "An experiment in linguistic synthesis
with a fuzzy logic controller," International Journal of Man-Machine
Studies, Vol. 7, No. 1, 1975 pp. 1-13.
[32] Sugeno, M., Industrial applications of fuzzy control, Elsevier Science
Pub. Co., 1985.
[33] James Kennedy and Russell C. Eberhart, "Swarm Intelligence", Morgan
Kaufmann Publishers, 2001.
[34] Mahamed G. H. Omran, "Particle Swarm Optimization Methods for
Pattern Recognition and Image Processing", PhD. thesis , University of
Pretoria, Pretoria, South Africa, November, 2004.
[35] Kennedy, J. and Eberhart, R., "Particle Swarm Optimization,"
Proceedings of the IEEE International Conference on Neural Networks,
Perth, Australia (1995), 1942-1945.
[36] R. Forrest, " Genetic Algorithms: Principles of Natural Selection
Applied to Computation," Science, Vol. 261, pp. 872-878, 1993.
[37] R. Storn and K. Price. Differential evolution - a simple and efficient
heuristic for global optimization over continuous spaces. Journal of
Global Optimization, 11:341-359, 1997.
[1] Yager, R. R. and Filev, D. P. "Essentials of Fuzzy Modeling and
Control," John Wiley, New York, U.S.A. (1994).
[2] Jang, J. S., Sun, C. T. and Mizutani, E., " Neuro-Fuzzy and Soft
Computing," Prentice Hall, New Jersey, U.S.A. (1997).
[3] J.C. Bezdek, "Pattern Recognition with Fuzzy Objective Function
Algorithm," Plenum Press, New York, 1981.
[4] J.C. Bezdek, "On the relationship between neural networks, pattern
recognition and intelligence," Int. J. Approx. Reasoning 6 (2) (1992) 85-
107.
[5] C.T. Lin, C.S.G. Lee, "Neural network-based fuzzy logic control and
decision system," IEEE Trans. Comput. 40 (12) (1991) 1320-1336.
[6] C.F. Juang, J.Y. Lin, C.T. Lin, "Genetic reinforcement learning through
symbiotic evolution for fuzzy controller design," IEEE Trans. Syst. Man
Cybern. Part B 30 (2) (2000) 290-302.
[7] C.L. Karr, "Design of an adaptive fuzzy logic controller using a genetic
algorithm," Proceedings of the Fourth International Conference on
Genetic Algorithm, San Diego, July 12-16, 1991, pp. 450-457.
[8] C.L. Karr, E.J. Gentry, "Fuzzy control of pH using genetic algorithm,"
IEEE Trans. Fuzzy Systems. 1 (1) (1993) 46-53.
[9] M. Setnes, H. Roubos, "GA-Fuzzy modeling and classification:
complexity and performance," IEEE Trans. Fuzzy Systems. 8 (4) (2000)
509-522.
[10] Mahamed G.H. Omran, Andries P Engelbrecht, and Ayed Salman,
"Dynamic Clustering using Particle Swarm Optimization with
Application in Unsupervised Image Classification," Proceeding of world
academy of science. Engineering and technology, volume 9, November
2005 ISSN 1307-6884.
[11] Chia-Chong Chen "Design of PSO-based Fuzzy Classification Systems",
Tamkang Journal of Science and Engineering," Vol. 9, No 1, (2006) pp.
63_70
[12] Ishibuchi, H., Nozaki, K., Yamamoto, N. and Tanaka, H., "Selecting
Fuzzy If-Then Rules for Classification Problems Using Genetic
Algorithms," IEEE Trans. Fuzzy Systems, Vol. 3, (1995) pp. 260_270.
[13] Jang, J. S., "ANFIS: Adaptive-Network-Based Fuzzy Inference
systems," IEEE Trans. on Systems, Man and Cybernetics, Vol. 23,
(1993) pp. 665_685.
[14] Nozaki, K., Ishibuchi, H. and Tanaka, H., "Adaptive Fuzzy Rule-Based
Classification Systems," IEEE Trans. on Fuzzy Systems, Vol. 4, No. 3,
Aug., (1996) pp. 238_250.
[15] Wang, L. X. and Mendel, J. M., "Generating Fuzzy Rules by Learning
from Examples," IEEE Trans. On Systems, Man and Cybernetics, Vol.
22, (1992) pp. 1414_ 1427.
[16] Wong, C. C. and Chen, C. C., "A GA-Based Method for Constructing
Fuzzy Systems Directly from Numerical Data," IEEE Trans. on Systems,
Man and Cybernetics- Part B: Cybernetics, Vol. 30, (2000) pp.
904_911.
[17] Simpson, P. K., "Fuzzy Min-Max Neural Networks- Part 1:
Classification," IEEE Trans. Neural Networks, Vol. 3, Sep., (1992) pp.
776_786.
[18] Wong, C. C. and Chen, C. C., "A Hybrid Clustering and Gradient
Descent Approach for Fuzzy Modeling," IEEE Trans. on Systems, Man
and Cybernetics-Part B: Cybernetics, Vol. 29, (1999) pp. 686_693.
[19] Xie, X.L., and G.Beni, "A validity measure for fuzzy clustering," IEEE
trans. Pattern Anal. Mach. Intell. 3(8): 1991 pp. 841-846.
[20] S. L. Chiu, "Fuzzy Model Identification Based on Cluster Estimation,"
Journal of Intelligent and Fuzzy Systems, 1994 pp. 267-278,.
[21] Emami, M.R. Turksen, Burhan. Goldenberg A.A.,. "Development of A
Systematic Methodology of Fuzzy Logic Modeling," IEEE transaction
on Fuzzy Systems, 6(3) 1998.
[22] Salehfar, H. et al., "A systematic approach to linguistic fuzzy modeling
based on input-output data," IEEE Proceedings of the 2000 Winter
Simulation Conference, 2000.
[23] Vachkov, G. and Fukuda, T. , "Multilevel composite fuzzy models," The
Ninth IEEE International Conference on Fuzzy Systems Volume 1, Issue
, 2000 pp. 453 - 458.
[24] Hwang, H.S., "Automatic design of fuzzy rule base for modeling and
control using evolutionary programming," IEE Proc-Control Theory
Appl, Vol 146, 1999.
[25] A. Homaifar, E. McCormick, "Simultaneous design of membership
functions and rule sets for fuzzy controller using genetic algorithms,"
IEEE Trans. Fuzzy Systems. 3 (2) (1995) 129-139.
[26] O. Cordon, F. Herrera, P. Villar, "Generating the knowledge base of a
fuzzy rule-based system by the genetic learning of the data base," IEEE
Trans. Fuzzy Systems. 9 (4) (2001) 667-674.
[27] S.Y. Ho, H.M. Chen, S.J. Ho, T.K. Chen, "Design of accurate classifiers
with a compact fuzzy-rule base using an evolutionary scatter partition of
feature space," IEEE Trans. Syst. Man Cybern. Part B 34 (2) (2004)
1031-1044.
[28] Enwang Zhou, Alireza Khotanzad, "Fuzzy classifier design using genetic
algorithms", Pattern Recognition 40 (2007) 3401 - 3414.
[29] Tanya Mirzayans, Nitin Parimi, Patrick Pilarski, Chris Backhouse,Loren
Wyard-Scott, Petr Musilek , "A Swarm Based System for Object
Recognition," Neural Network World 3/05, 2005, pp. 243-255.
[30] Ata E. Zadeh Shermeh and Reza Ghaderi, "An Intelligent System for
Classification of the Communication Formats Using PSO," Informatica
32 (2008) pp. 213-218.
[31] Mamdani, E.H. and S. Assilian, "An experiment in linguistic synthesis
with a fuzzy logic controller," International Journal of Man-Machine
Studies, Vol. 7, No. 1, 1975 pp. 1-13.
[32] Sugeno, M., Industrial applications of fuzzy control, Elsevier Science
Pub. Co., 1985.
[33] James Kennedy and Russell C. Eberhart, "Swarm Intelligence", Morgan
Kaufmann Publishers, 2001.
[34] Mahamed G. H. Omran, "Particle Swarm Optimization Methods for
Pattern Recognition and Image Processing", PhD. thesis , University of
Pretoria, Pretoria, South Africa, November, 2004.
[35] Kennedy, J. and Eberhart, R., "Particle Swarm Optimization,"
Proceedings of the IEEE International Conference on Neural Networks,
Perth, Australia (1995), 1942-1945.
[36] R. Forrest, " Genetic Algorithms: Principles of Natural Selection
Applied to Computation," Science, Vol. 261, pp. 872-878, 1993.
[37] R. Storn and K. Price. Differential evolution - a simple and efficient
heuristic for global optimization over continuous spaces. Journal of
Global Optimization, 11:341-359, 1997.
@article{"International Journal of Electrical, Electronic and Communication Sciences:50648", author = "Hassan M. Elragal", title = "Using Swarm Intelligence for Improving Accuracy of Fuzzy Classifiers", abstract = "This paper discusses a method for improving accuracy
of fuzzy-rule-based classifiers using particle swarm optimization
(PSO). Two different fuzzy classifiers are considered and optimized.
The first classifier is based on Mamdani fuzzy inference system
(M_PSO fuzzy classifier). The second classifier is based on Takagi-
Sugeno fuzzy inference system (TS_PSO fuzzy classifier). The
parameters of the proposed fuzzy classifiers including premise
(antecedent) parameters, consequent parameters and structure of
fuzzy rules are optimized using PSO. Experimental results show that
higher classification accuracy can be obtained with a lower number
of fuzzy rules by using the proposed PSO fuzzy classifiers. The
performances of M_PSO and TS_PSO fuzzy classifiers are compared
to other fuzzy based classifiers", keywords = "Fuzzy classifier, Optimization of fuzzy systemparameters, Particle swarm optimization, Pattern classification.", volume = "4", number = "8", pages = "1115-8", }
