A maximizing-discriminability-based self-organizing fuzzy network for classification problems

IEEE Transactions on Fuzzy Systems

Volumn 18, Issue 2, 2010, Pages 362-373

  Wu, Gin Der ^a   Huang, Pang Hsuan ^a  

^a NATIONAL CHI NAN UNIVERSITY   (Taiwan)

Author keywords

Fuzzy network; Gaussianmixture model (GMM); Gradient descent method; Linear discriminant analysis (LDA); Principal component analysis (PCA); Support vector machine (SVM)

Indexed keywords

COMPARATIVE PERFORMANCE ANALYSIS; DISCRIMINABILITY; FUZZY NETWORKS; GAUSSIAN MIXTURE MODEL; GRADIENT-DESCENT; LINEAR DISCRIMINANT ANALYSIS; NOISE TYPES; PARAMETER LEARNING; SELF-ORGANIZING FUZZY; TWO CLASSIFICATION;

COST REDUCTION; DISCRIMINANT ANALYSIS; FUZZY CONTROL; FUZZY RULES; IMAGE RETRIEVAL; MAGNETOSTRICTIVE DEVICES; MULTILAYER NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77950665021     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2010.2042061     Document Type: Article

References (37)

1. C. F. Juan, "A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms," IEEE Trans. Fuzzy Syst., vol.10, no.2, pp. 155-170, Apr. 2002.
2. C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neural-Fuzzy Synergism to Intelligent Systems. Englewood Cliffs, NJ: Prentice-Hall, May 1996.
3. L. Rutkowski and K. Cpalka, "Designing and learning of adjustable quasitriangular norms with applications to neuro-fuzzy systems," IEEE Trans. Fuzzy Syst., vol.13, no.1, pp. 140-151, Feb. 2005. (Pubitemid 40319316)
4. M. Y. Chen and D. A. Linkens, "Rule-base self-generation and simplification for data-driven fuzzy models," Fuzzy Sets Syst., vol.142, pp. 243-265, Mar. 2004.
5. J. S.Wang and C. S. G. Lee, "Self-adaptive neuro-fuzzy inference systems for classification applications," IEEE Trans. Fuzzy Syst., vol.10, no.6, pp. 790-802, Dec. 2002.
6. L. I.Kuncheva, "Howgood are fuzzy IF-THEN classifiers?," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.30, no.4, pp. 501-509, Aug. 2000.
7. H. Ishibuchi and T. Nakashima, "Effect of rule weights in fuzzy rulebased classification systems," IEEE Trans. Fuzzy Syst., vol.9, no.4, pp. 506-515, Aug. 2001. (Pubitemid 32935694)
8. B. Gabrys and A. Bargiela, "General fuzzy min-max neural network for clustering and classification," IEEE Trans. Neural Netw., vol.11, no.3, pp. 769-783, May 2000. (Pubitemid 32070254)
9. K. Nozaki, H. Ishibuchi, and H. Tanaka, "Adaptive fuzzy rule-based classification system," IEEE Trans. Fuzzy Syst., vol.4, no.4, pp. 238-250, Aug. 1996. (Pubitemid 126777743)
10. V. Vapnik, The Nature of Statistical Learning Theory. NewYork: Springer-Verlag, 1995.
11. C. Cortes and V. Vapnik, "Support vector networks," Int. J. Mach. Learn., vol.20, pp. 1-25, 1995.
12. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press, 2000.
13. C. Burges, "Atutorial on support vector machines for pattern recognition," Data Mining Knowl. Discov., vol.2, no.2, pp. 121-167, 1998. (Pubitemid 128695475)
14. C.W. Hsu and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Netw., vol.13, no.2, pp. 415-425, Mar. 2002. (Pubitemid 34475042)
15. K. M. Lin and C. J. Lin, "A study on reduced support vector machines," IEEE Trans. Neural Netw., vol.14, no.6, pp. 1449-1459, Nov. 2002.
16. O. Chapelle, P. Haffner, and V. Vapnik, "Support vector machines for histogram-based image classification," IEEE Trans. Neural Netw., vol.10, no.5, pp. 1055-1064, Sep. 1999.
17. B. Schölkopf, K. Sung, C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, "Comparing support vector machines with Gaussian kernels to radial basis function classifiers," IEEE Trans. Signal Process., vol.45, no.11, pp. 2758-2765, Nov. 1997. (Pubitemid 127766345)
18. J. H. Chiang and P. Y. Hao, "Support vector learning mechanism for fuzzy rule-based modeling: A new approach," IEEE Trans. Fuzzy Syst., vol.12, no.1, pp. 1-12, Feb. 2004.
19. Y. Chen and J. Z. Wang, "Support vector learning for fuzzy rule-based classification systems," IEEE Trans. Fuzzy Syst., vol.11, no.6, pp. 716- 728, Dec. 2003.
20. C. T. Lin, C. M. Yeh, and C. F. Hsu, "Fuzzy neural network classification using support vector machine," in Proc. IEEE Symp. Circuits Syst., 2004, pp. 724-727.
21. C. T. Lin, C. M. Yeh, S. F. Liang, J. F. Chung, and N. Kumar, "Supportvector- based fuzzy neural network for pattern classification, " IEEE Trans. Fuzzy Syst., vol.14, no.1, pp. 31-41, Feb. 2006. (Pubitemid 43263926)
22. C. F. Juang, S. H. Chiu, and S.W. Chang, "A self-organizing TS-type fuzzy network with support vector learning and its application to classification problems," IEEE Trans. Fuzzy Syst., vol.15, no.5, pp. 997-1008, Oct. 2007.
23. J. W. Hung, H. M. Wang, and L. S. Lee, "Comparative analysis for datadriven temporal filters obtained via principal component analysis (PCA) and linear discriminant analysis (LDA) in speech recognition," presented at the Eurospeech Conf., Aalborg, Denmark, 2001.
24. C. F. Juang and C. T. Lin, "An on-line self-constructing neural fuzzy inference network and its applications," IEEE Trans. Fuzzy Syst., vol.6, no.1, pp. 12-32, Feb. 1998. (Pubitemid 128747936)
25. G. D. Wu and C. T. Lin, "Word boundary detection with mel-scale frequency bank in noisy environment," IEEE Trans. Speech Audio Process., vol.8, no.5, pp. 541-554, Sep. 2000.
26. S. van Vuuren and H. Hermansky, "Data-driven design of RASTA-like filters," presented at the Eurospeech Conf., Rhodes, Greece, 1997.
27. C. Avendano, S. van Vuuren, and H. Hermansky, "Data based filter design for RASTA-like channel normalization in ASR," presented at the ICSLP, Philadelphia, PA, 1996.
28. M. L. Shire, "Data-driven modulation filter design under adverse acoustic conditions and using phonetic and syllabic units," presented at the Eurospeech Conf., Budapest, Hungary, 1999.
29. R. Haeb-Umbach, D. Geller, and H. Ney, "Improvements in connected digit recognition using linear discriminant analysis and mixture densities," in Proc. ICASSP, 1993, pp. 239-242. (Pubitemid 23674445)
30. J.W. Hung and L. S. Lee, "Data-driven temporal filters for robust features in speech recognition obtained via minimum classification error (MCE)," in Proc. ICASSP, 2002, pp. I-373-I-376.
31. J. W. Hung and L. S. Lee, "Optimization of temporal filters for constructing robust features in speech recognition," IEEE Trans. Speech Audio Process., vol.14, no.3, pp. 808-832, May 2006.
32. C. H. Chen, C. T. Lin, and C. J. Lin, "A functional-link-based fuzzy neural network for temperature control," in Proc. IEEE Symp. FOCI, Apr. 2007, pp. 53-58. (Pubitemid 47447713)
33. C. C. Chang and C. J. Lin, (2001). LIBSVM: A library for support vector machines, Software [Online]. Available: http://www.csie.ntu. edu.tw/tild;cjlin/libsvm
34. C. T. Lin, H.W. Nein, and J. Y. Hwu, "GA-based noisy speech recognition using two-dimensional cepstrum," IEEE Trans. Speech Audio Process., vol.8, no.6, pp. 664-675, Nov. 2000. (Pubitemid 32025314)
35. A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-Time Signal Processing, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2000.
36. M. G. Varga and H. J. M. Steeneken, "Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems," Comput. Speech Language, vol.12, pp. 247-251, 1993.
37. G. D. Wu and C. T. Lin, "A recurrent neural fuzzy network for word boundary detection in variable noise-level environments," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.31, no.1, pp. 84-97, Feb. 2001.