Construction of neurofuzzy models for imbalanced data classification

IEEE Transactions on Fuzzy Systems

Volumn 22, Issue 6, 2014, Pages 1472-1488

  Gao, Ming ^a   Hong, Xia ^a   Harris, Chris J ^b  

^a UNIVERSITY OF READING   (United Kingdom)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Cross validation; forward selection; identification; imbalanced datasets; leave one out (LOO); neurofuzzy model (NFM)

Indexed keywords

EID: 84914167532     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2013.2296091     Document Type: Article

References (47)

1. M. Stone, "Cross-validatory choice and assessment of statistical predictions," J. Roy. Statist. Soc. Series B (Methodological), vol. 36, no. 2, pp. 111-147, 1974.
2. R. H. Myers, Classical and Modern Regression With Applications, 2nd ed. ed. Boston, MA, USA: PWS-KENT, 1990.
3. S. Chen, S. A. Billings, and W. Luo, "Orthogonal least squares methods and their applications to non-linear system identification," Int. J. Contr., vol. 50, pp. 1873-1896, 1989.
4. X.Hong, P. M. Sharkey, andK.Warwick, "Automatic nonlinear predictive model construction using forward regression and the PRESS statistic," Proc. Inst. Elect. Eng. - Contr. Theory Appl., vol. 150, no. 3, pp. 245-254, 2003.
5. X. Hong, S. Chen, and C. J. Harris, "A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate," Int. J. Syst. Sci., vol. 39, no. 2, pp. 119-25, 2008.
6. L. A. Zadeh, "Fuzzy sets," Inf. Contr., vol. 8, no. 1, pp. 338-353, 1965.
7. S. Guillaume, "Designing fuzzy inference systems from data: An interpretability-oriented review," IEEE Trans. Fuzzy Syst., vol. 9, no. 3, pp. 426-443, Jun. 2001.
8. M. Gan, M. Hanmandlu, and A. H. Tan, "From a gaussian mixture model to additive fuzzy systems," IEEE Trans. Fuzzy Syst., vol. 13, no. 3, pp. 303-316, Jun. 2005.
9. E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," Int. J. Man-Mach. Stud., vol. 51, pp. 135-147, 1999.
10. P. M. Larsen, "Industrial applications of fuzzy logic control," Int. J. Man-Mach. Stud., vol. 12, no. 1, pp. 3-10, 1980.
11. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst., Man, Cybern., vol. SMC-15, no. 1, pp. 116-132, Jan./Feb. 1985.
12. M. F. Azeem, M. Hanmandlu, and N. Ahmad, "Generalization of adaptive neuro-fuzzy inference systems," IEEE Trans. Neural Netw., vol. 11, no. 6, pp. 1332-1346, Nov. 2000.
13. M. Brown and C. Harris, Neurofuzzy Adaptive Modelling and Control. Hertfordshire, U.K: Prentice-Hall, 1994.
14. C. Harris, X. Hong, and Q. Gan, Adaptive Modelling, Estimation and Fusion From Data: A Neurofuzzy Approach. London, U.K.: Springer-Verlag, 2002.
15. J. R. Castro, O.Castillo, P.Melin, and A. R. Díaz, O. Mendoza, "Universal approximation of a class of interval type-2 fuzzy neural networks illustrated with the case of non-linear identification," in Proc. IFSA/EUSFLAT Conf., Corfu, Greece, 2009, pp. 1382-1387.
16. M. Figueiredo, R. Ballini, S. Soares, M. Andrade, and F. Gomide, "Learning algorithms for a class of neurofuzzy network and application," IEEE Trans. Syst., Man, Cybern. C: Appl. Rev., vol. 34, no. 3, pp. 293-301, Aug. 2004.
17. H. Roubos and M. Setnes, "Compact and transparent fuzzy models and classifiers through iterative complexity reduction," IEEE Trans. Fuzzy Syst., vol. 9, no. 4, pp. 516-524, Aug. 2001.
18. J. Roubos, M. Setnes, and J. Abonyi, "Learning fuzzy classification rules from labeled data," Inf. Sci., vol. 150, pp. 77-93, 2003.
19. S. Mohagheghi, G. Venayagamoorthy, and R. Harley, "Fully evolvable optimal neurofuzzy controller using adaptive critic designs," IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1450-1461, Dec. 2008.
20. M. Gao, X. Hong, and C. J. Harris, "A unified neurofuzzy model for classification," Int. J. Syst. Sci., pp. 1-14, 2013, in press. DOI: 10.1080/00207721.2013.763301.
21. C. M. Bishop, Pattern Recognition and Machine Learning. NewYork, NY, USA: Springer-Verlag New York, Inc., 2006.
22. J. Yen and L.Wang, "Simplifying fuzzy rule-based models using orthogonal transformationmethods," IEEE Trans. Syst., Man, Cybern. B: Cybern., vol. 29, no. 1, pp. 13-24, Feb. 1999.
23. Y. Shi, R. Eberhart, and Y. Chen, "Implementation of evolutionary fuzzy systems," IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 109-119, Apr. 1999.
24. M. Setnes and H. Roubos, "GA-fuzzy modeling and classification: Complexity and performance," IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 509-522, Oct. 2000.
25. X. Hong and C. J. Harris, "A neurofuzzy network knowledge extraction and extended Gram-Schmidt algorithm for model subspace decomposition," IEEE Trans. Fuzzy Syst., vol. 11, no. 4, pp. 528-541, Aug. 2003.
26. N. Petrick, H. P. Chan, B. Sahiner, and D. Wei, "An adaptive densityweighted contrast enhancement filter for mammographic breast mass detection," IEEE Trans. Med. Imag., vol. 15, no. 1, pp. 59-67, Feb. 1996.
27. T. Fawcett and F. Provost, "Adaptive fraud detection," Data Mining Knowl. Discov., vol. 1, pp. 291-316, 1997.
28. E. P. D. Pednault, B. K. Rosen, and C. Apte, "Handling imbalanced datasets in insurance risk modeling," IBM Res. Rep. RC-21731, Tech. Rep., 2000.
29. M.Kubat, R. C. Holte, and S.Matwin, "Machine learning for the detection of oil spills in satellite radar images," Mach. Learn., vol. 30, pp. 195-215, 1998.
30. F. Provost, "Machine learning from imbalanced datasets 101," in Proc. AAAI Worksh. Learn. Imbalanced Data Sets, 2000.
31. N. V. Chawla, K. W. Bowyer, L. O.Hall, andW. P.Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique," J. Artif. Intell. Res., vol. 16, pp. 321-357, 2002.
32. Z. Ferandez, M. J. Jesus, and F. Herrera, "On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data sets," Expert Syst. Appl., vol. 36, pp. 9805-9812, 2009.
33. M. Gao, X. Hong, S. Chen, and C. J. Harris, "A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems," Neurocomput., vol. 74, no. 17, pp. 3456-3466, 2011.
34. M. Gao, X. Hong, S. Chen, and C. Harris, "Probability density function estimation based over-sampling for imbalanced two-class problems," in Proc. Int. Joint Conf. Neural Netw., 2012, pp. 1-8.
35. A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recogn., vol. 30, pp. 1145-1159, 1997.
36. C. van Rijsbergen, Information Retrieval. London, U.K.: Butterworths, 1979.
37. X. Hong, S. Chen, and C. J. Harris, "A kernel-based two-class classifier for imbalanced data sets," IEEE Trans. Neural Netw., vol. 18, no. 1, pp. 28-41, Jan. 2007.
38. C. Elkan, "The foundations of cost-sensitive learning," in Proc. 17th Int. Joint Conf. Artif. Intell., Seattle, WA, USA, 2001, pp. 973-978.
39. K. M. Ting, "An instance-weighting method to induce cost-sensitive trees," IEEE Trans. Knowl. Data Eng., vol. 14, no. 3, pp. 659-665, May/Jun. 2002.
40. Q. Gan and C. J. Harris, "Fuzzy local linearization and local basis function expansion in nonlinear system modeling," IEEE Trans. Syst., Man, Cybern., vol. 29, no. 4, pp. 559-565, Aug. 1999.
41. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statist. Soc., Series B, vol. 39, no. 1, pp. 1-38, 1977.
42. M. Gao, X. Hong, and C. Harris, "A neurofuzzy classifier for two class problems," in Proc. 12th U.K. Workshop Computat. Intell., 2012, pp. 1-6.
43. A. Frank and A. Asuncion. (2010). "UCI machine learning repository," [Online]. Available: http://archive.ics.uci.edu/ml
44. K. K. Lee, C. J. Harris, S. R. Gunn, and P. Reed, "Classification of imbalanced data with transparent kernel," in Proc. INNS-IEEE Int. Joint Conf. Neural Netw., Washington, DC, USA, 2001, pp. 2410-2415.
45. J. Moody and C. J. Darken, "Fast learning in networks of locally-tuned processing units," Neural Computat., vol. 1, pp. 281-294, 1989.
46. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Upper Saddle River, NJ, USA: Prentice-Hall, 1998.
47. M. Gao, X. Hong, S. Chen, and C. Harris, "On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems," in Proc. Int. Joint Conf. Neural Netw., 2011, pp. 1146-1153.