Takagi-Sugeno-Kang fuzzy classifiers for a special class of time-varying systems

IEEE Transactions on Fuzzy Systems

Volumn 16, Issue 4, 2008, Pages 1038-1049

  Mikut, Ralf ^a   Burmeister, Ole ^a   Gröll, Lutz ^a   Reischl, Markus ^a  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

Brain; Fuzzy systems; Man machine systems; Pattern classification; Prosthetics; Time series; Time varying systems

Indexed keywords

BENCHMARKING; CLASSIFIERS; FEEDBACK CONTROL; MACHINE DESIGN; TIME SHARING SYSTEMS; TIME VARYING CONTROL SYSTEMS; TIME VARYING SYSTEMS;

BRAIN; FUZZY SYSTEMS; MAN-MACHINE SYSTEMS; PATTERN CLASSIFICATION; PROSTHETICS; TIME SERIES;

CLASSIFICATION (OF INFORMATION);

EID: 50549098605     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2008.917291     Document Type: Article

References (53)

1. E. J. Keogh and M. J. Pazzani, "An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback," in Proc. 4th Int. Conf. Knowl. Discovery Data Mining, New York: ACM Press, 1998, pp. 239-241.
2. C. Dietrich, F. Schwenker, and G. Palm, "Classification of time series utilizing temporal and decision fusion," in Proc. Multiple Classifier Syst., Lecture Notes in Computer Science, vol. 2096. New York: Springer-Verlag, 2001, pp. 378-387.
3. C. Dietrich, "Temporal sensorfusion for the classification of bioacustic time series," Ph.D. dissertation, Universität Ulm, Ulm, Germany.
4. W. H. Hsu, S. R. Ray, and D. C. Wilkins, "A multistrategy approach to classifier learning from time series," Mach. Learning, vol. 36, no. 1/2, pp. 213-236, 2000.
5. P. Geurts, "Pattern extraction for time series classification," in Proc. Principles Data Mining Knowl. Discoverv: 5th Eur. Conf, PKDD 2001 Freiburg Germany, L. D. Raedt and A. Siebes, Eds. New York: Springer-Verlag, pp. 115-127.
6. M. Kadous, "Temporal classification: Extending the classification paradigm to multivariate time series," Ph.D.dissertation, Univ. New South Wales, Sydney, Australia, 2002.
7. R. Mikut, M. Reischl, O. Burmeister, and T. Loose, "Data mining in medical time series," Biomedizinische Technik, vol. 51, no. 5/6, pp. 288-293, 2006.
8. E. Keogh and C. Ratanamahatana, "Exact indexing of dynamic time warping," Knowl. Inf. Syst., vol. 7, pp. 358-386, 2005.
9. S. Wolf, T. Loose, M. Schablowski, L. Döderlein, R. Rupp, H. J. Gerner, G. Bretthauer, and R. Mikut, "Automated feature assessment in instrumented gait analysis," Gait Posture, vol. 23, no. 3, pp. 331-338, 2006.
10. R. Mikut. J. Jäkel, and L. Gröll, "Interpretability issues in data-based learning of fuzzy systems," Fuzzy Sets Syst., vol. 150, no. 2, pp. 179-197, 2005.
11. M. Reischl, L. Gröll, and R. Mikut, "Optimized classification of multiclass problems applied to EMG-control of hand prostheses," in Proc. IEEE Int. Joint Conf. Neural Netw., Budapest, Hungary, 2004, pp. 1473-1478.
12. H. Keller and A. Fick, "Machine modelling of complex dynamic systems," Syst. Anal. Model. Simul., vol. 39, no. 2, pp. 205-214, 2000.
13. A. Kehagias and V. Petridis, "Predictive modular neural networks for time series classification," Neural Netw., vol. 10, no. 1, pp. 31-49, 1997.
14. R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artif. Intell., vol. 97, no. 1/2, pp. 273-324, 1997.
15. A. L. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artif. Intell., vol. 97, no. 1/2, pp. 245-271, 1997.
16. M. M. Tatsuoka, Multivariate Analysis. New York:, Macmillan, 1988.
17. A. Hyvärinen, "Survey on independent component analysis," Neural Comput. Surveys, vol. 2, pp. 94-128, 1999.
18. A. K. Jain, R. P. W. Duin and J. Mao, "Statistical pattern recognition: A review," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 4-36, Jan. 2000.
19. J. Kohlmorgen and B. Blankertz, "Bayesian classification of single-trial event-related potentials in EEG," Int. J. Bifurcations Chaos, vol. 14, no. 2, pp. 719-726, 2004.
20. J. Rodríguez, C. Alonso, and J. Maestro, "Support vector machines of interval-based features for time series classification," Knowl.-Based Syst., vol. 18, no. 4/5, pp. 171-178, 2005.
21. S. Lemm, C. Schäfer, and G. Curio, "BCl competition 2003 - Data set III: Probabilistic modeling of sensorimotor μ rhythms for classification of imaginery hand movements," IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1077-1080, Jun. 2004.
22. G. R. Müller-Putz, R. Scherer, C. Neuper, and G. Pfurtscheller, "Steady-state somatosensory evoked potentials: Suitable brain signals for brain-computer interfaces?" IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, no. 1, pp. 30-37, Mar. 2006.
23. L. Xu, A. Krzyzak, and C. Suen, "Methods for combining multiple classifiers Trans. Syst., Man, Cybern., vol. 22, no. 3, pp. 418-435, May 1992.
24. L. I. Kuncheva, "Switching between selection and fusion in combining classifiers: An experiment," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 32, no. 2, pp. 146-156, apr. 2002.
25. L. I. Kuncheva, "How good are fuzzy if-then classifiers?" IEEE Trans. Syst., man, Cybern. B, Cybern., vol. 30, no. 4, pp. 501-509, aug. 2000.
26. M. Cococcioni, B. Lazzerini, and F. Marcelloni, "A TSK fuzzy model for combining outputs of multiple classifiers," in Proc. IEEE Annu. Meeting NAFIPS, 2004, pp. 871-876.
27. R. E. Banfield, L. O. Hall, K. W. Bowyer, and W. P. Kegelmeyer, "Ensembles of classifiers from spatially disjoint data," in Proc. 6th Int. Conf. Multiple Classifier Syst., Monterey, CA, 2005, pp. 190-205.
28. O. Cordón, F. Herrera, F. Hoffmann, and L. Magdalena, "Genetic fuzzy systems - Evolutionary tuning and learning of fuzzy knowledge bases," in Advances in Fuzzy Systems - Apllications and Theory, vol. 19, 2001, Singapore: World Scientific, 2001.
29. M. Setnes, "Supervised fuzzy clustering for rule extraction," IEEE Trans. Fuzzy Syst., vol. 8, no. 4, pp. 416-424, Aug. 2000.
30. S. Osowski, T. H. Linh, and K. Brudzewski, "Neuro-fuzzy TSK network for calibration of semiconductor sensor array for gas measurements," IEEE Trans. Instrum. Meas., vol. 53, no. 3, pp. 630-637, Jun. 2004.
31. J. Yen, L. Wang, and C. W. Gillespie, "Improving the interpretability of TSK fuzzy models by combining global learning and local learning," IEEE Trans. Fuzzy Syst., vol. 6. no. 4, pp. 530-537, Nov. 1998.
32. S. Beck, R. Mikut, and J. Jäkel, "A cost-sensitive learning algorithm for fuzzy rule-based classifiers," Mathware Soft Comput. vol. 11, no. 2/3, pp. 175-195, 2005.
33. L. I. Kuncheva, J. C. Bezdek, and R. P. W. Duin, "Decision templates for multiple classifier fusion: An experimental comparison," Pattern Recog., vol. 4, no. 2, pp. 299-314, 2001.
34. F. Hoffmann and O. Nelles, "Genetic programming for model selection of TSK-fuzzy systems," Inf. Sci., vol. 136, no. 1-4, pp. 7-28, 2001.
35. M. Setnes and J. Roubos, "GA-fuzzy modeling and classification: Complexity and performance," IEEE Trans. Fuzzy Syst., vol. 8, no. 5, pp. 509-522, Oct. 2000.
36. J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
37. F. Höppner, F. Klawonn, and R. Kruse, Fuzzy Cluster Analysis. New York: Wiley, 1999.
38. R. Mikut. T. Loose. O. Burmeister. S. Braun, and M. Reischl. (2007), Gait-CAD [Online]. Available: http://www.iai.fzk.de/www/extern/ index.php?id=657&L=1
39. S. Hettich and S. D. Bay. (1999). The UCI KDD archive. Irvine, CA: University of California, Department of Information and Computer Science, [Online]. Available: http://kdd.ics.uci.edu
40. M. Kudo, J. Toyama, and M. Shimbo, "Multidimensional curve classification using passing-through regions," Pattern Recog. Lett. vol. 20, no. 11-13, pp. 1103-1111, 1999.
41. M. W. Kadous and C. Sammut, "Classification of multivariate time series and structures data using constructive induction," Mach. Learning vol. 58, no. 1/2, pp. 179-216, 2005.
42. P. Geurts and L. Wehenkel, "Segment and combine approach for non-parametric time-series classification," in Proc. 9th Eur. Conf. Principles Practice Knowl. discovery Databases, New York: Springer-Verlag, 2005, pp. 478-485.
43. J. A. González and J. J. R. Diez, "Boosting interval-based literals: Variable length and early classification," in Data Mining in Time Series Databases, M. Last, A. Kandel, and H. Bunke, Eds. Singapore: World Scientific, 2004.
44. N. Saito, "Local feature extraction and its applications using a library of bases," Ph.D. dissertation, Dept. Math., Yale Univ., New Haven, CT, 1994.
45. R. T. Olszewski, "Generalized feature extraction for structural pattern recognition in time-series data," Ph.D. dissertation, Carnegie Mellon Univ., Pittsburgh, PA, 2001.
46. C. E. Shannon, "A mathematical theory of communication," Bell Syst. Tech. J., vol. 27, pp. 379-423, 1948
47. J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan, "Brain-computer interface technology: A review of the first international meeting," IEEE Irans. Rehabil. Eng., vol. 8, no. 2, pp. 164-173, Jun. 2000.
48. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control," Clin. Neurophysiol., vol. 113, pp. 767-791, 2002.
49. G. R. Müller-Putz, R. Scherer, G. Pfurtscheller, and R. Rupp, "EEG-based neuroprosthesis control: A step towards clinical practice," Neurosci. Lett., vol. 382, pp. 169-174, 2005.
50. K. Tanaka, K. Matsunaga, and H. O. Wang, "Electroencephalogram-based control of an electric wheelchair," IEEE Trans. Robot., vol. 21, no. 4, pp. 762-766, Aug. 2005.
51. R. Leeb, S. Scherer, F. Lee, H. Bischof, and G. Pfurtscheller, "Navigation in virtual environments through motor imagery," in Proc. 9th, Comput. Vision Winter Workshop, Piran, Slowenian, Slovenian Pattern Recognition Society, 2004, pp. 99-108.
52. B. Blankertz, K.-R. Müller, D. J. Krusienski, G. Schalk, J. R. Wolpaw, A. Schlögl, G. Pfurtscheller, J. D. R. Millán, M. Schröder, and N. Birbaumer, "The BCI competition III: Validating alternative approaches to actual BCI problems," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, no. 2, pp. 153-159, Jun. 2006
53. O. Burmeister, M. Reischl, and R. Mikut, "Application of time-variant classifiers to invasively recorded signals from brain and peripheral nerve," Biomedizinische Technik, vol. 52, 2007. [CD Rom].