Evolving fuzzy optimally pruned extreme learning machine for regression problems

Evolving Systems

Volumn 1, Issue 1, 2010, Pages 43-58

  Pouzols, Federico Montesino ^a   Lendasse, Amaury ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Evolving fuzzy systems; Evolving Takagi Sugeno; Extreme learning machine; Optimally pruned extreme learning machine; Regression; Time series

Indexed keywords

APPROACH SYSTEM; BENCHMARK DATASETS; COMPARATIVE ANALYSIS; EVOLVING FUZZY SYSTEMS; EVOLVING TAKAGI-SUGENO; EXTREME LEARNING MACHINE; FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; FUZZY BASIS FUNCTIONS; HIDDEN LAYERS; HIDDEN NEURONS; IDENTIFICATION METHOD; NEURO-FUZZY METHODS; NEUROFUZZY SYSTEM; RANDOM PROJECTIONS; REGRESSION; REGRESSION PROBLEM; TAKAGI-SUGENO SYSTEMS;

FUZZY SETS; LEARNING ALGORITHMS; NEURAL NETWORKS; REGRESSION ANALYSIS; TIME SERIES;

LEARNING SYSTEMS;

EID: 79952093417     PISSN: 18686478     EISSN: 18686486     Source Type: Journal    
DOI: 10.1007/s12530-010-9005-y     Document Type: Article

References (57)

1. Achlioptas D (2003) Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J Comput Syst Sci 66(4):671-687.
2. Angelov P, Filev D (2004) Flexible models with evolving structure. Int J Intell Syst 19(4):327-340.
3. Angelov P, Filev D, Kasabov N (2008) Evolving fuzzy systems-preface to the special section. IEEE Trans Fuzzy Syst 16(6):1390-1392.
4. Angelov P, Filev D, Kasabov N (eds) (2010) Evolving intelligent systems. Methodoloy and applications. Wiley/IEEE Press.
5. Angelov PP, Filev DP (2004) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern B 34(1):484-498.
6. Angelov PP, Filev DP (2005) Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models. In: Proceedings of the IEEE International Conference on fuzzy systems, Reno, NV, USA, pp 1068-1073.
7. Asuncion A, Newman DJ (2010) UCI machine learning repository. University of California, Irvine, Center for Machine Learning and Intelligent Systems. http://archive.ics.uci.edu/ml/.
8. Birattari M, Bontempi G, Bersini H (1999) Lazy learning meets the recursive least squares algorithm. In: Advances in Neural Information Processing Systems (NIPS), vol 11. MIT Press, Cambridge, pp 375-381.
9. Dourado A, Aires L, Victor J (2009) eFSLab: Developing evolving fuzzy systems from data in a friendly environment. In: Proc 10th Eur Control Conf, Prague, Czech Republic, pp 922-927.
10. Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407-499.
11. ESTSP07 (2010) ESTSP: European Symposium on Time Series Prediction. http://www.estsp.org.
12. Feng G, Huang GB, Lin Q, Gay R (2009) Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans Neural Netw 20(8):1362-1357.
13. Fradkin D, Madigan D (2003) Experiments with random projections for machine learning. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, pp 517-522.
14. Haykin SS (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs. ISBN: 978-0132733502.
15. Higham NJ (2002) Accuracy and Stability of Numerical Algorithms, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia.
16. Huang GB (2008) Reply to "comment on the extreme learning machine". IEEE Trans Neural Netw 19(8):1495-1496.
17. Huang GB, Chen L, Siew CK (2006) Universal approximaion using incremental constructuve feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879-892.
18. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1-3):489-5-01.
19. Hurrell JW, Deser C (2009) North atlantic climate variability: the role of the north atlantic oscillation. J Mar Syst 78(1):28-41.
20. Hyndman RJ (2010) Time series data library. http://www.robjhyndman.com/TSDL.
21. Internet2Observatory (2008) The Internet2 Observatory. http://www.internet2.edu/observatory/.
22. Kasabov N (2007) Evolving connectionist systems: the knowledge engineering approach, 2nd edn. Springer, Berlin.
23. Kasabov NK, Song Q (2002) DENFIS: dynamic evolving neuralfuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144-154.
24. Leite D, Costa P, Gomide F (2009) Interval-based evolving modeling. In: IEEE Workshop on evolving and self-developing intelligent systems, Nasville, TN, USA, pp 1-8.
25. Lendasse A, Sorjamaa A, Miche Y (2010) The OP-ELM toolbox. Time Series Prediction and Chemoinformatics Group. Department of Information and Computer Science. Aalto University School of Science and Technology. http://www.cis.hut.fi/projects/tsp/index.php?page=opelm.
26. Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411-1423.
27. Lughofer ED (2008) Flexfis: A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models. IEEE Trans Fuzzy Syst 16(6):1393-1410.
28. Mackey MC, Glass L (1977) Oscillations and chaos in physiological control systems. Science 197(4300):287-289.
29. Miche Y, Sorjamaa A, Lendasse A (2008) OP-ELM: Theory, experiments and a toolbox. In: Proceedings of the international conference on artificial neural networks. Lecture notes in computer science, vol 5163, Prague, Czech Republic, pp 145-154.
30. Miche Y, Schrauwen B, Lendasse A (2010) Machine learning techniques based on random projections. In: 18th European symposium on artificial neural networks, computational intelligence and machine learning, pp 295-302.
31. Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158-162.
32. Mikut R, Jakel J, Groll L (2005) Interpretability issues in data-based learning of fuzzy systems. Fuzzy Sets Syst 150(2):179-197.
33. Montesino Pouzols F, Lendasse A, Barriga A (2008) Fuzzy Inference Based Autoregressors for Time Series Prediction Using Nonparametric Residual Variance Estimation. In: Proceedings of the IEEE International conference on fuzzy systems, Hong Kong, China, pp 613-618.
34. Montesino Pouzols F, Lendasse A, Barriga A (2008) xftsp: a tool for time series prediction by means of fuzzy inference systems. In: Proceedings of the IEEE international conference on intelligent systems, Varna, Bulgaria, pp 2-2-2-7.
35. Montesino Pouzols F, Lendasse A, Barriga A (2010) Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation. Fuzzy Sets Syst 161(4):471-497.
36. Moreno-Velo FJ, Baturone I, Barriga A, Sánchez-Solano S (2007) Automatic tuning of complex fuzzy systems with Xfuzzy. Fuzzy Sets Syst 158(18):2026-2038.
37. Myers RH (2000) Classical and modern regression with applications, 2nd edn. Duxbury Press, North Scituate.
38. Pedrycz W (2005) Knowledge-based clustering: from data to information granules. Wiley, New York.
39. Platt J (1991) A resource-allocating network for function interpolation. Neural Comput 3(2):213-225.
40. Poggio T, Girosi F (1989) A Theory of Networks for Approximation and Learning, vol 1140. MIT Press, Cambridge.
41. Ramos JV, Dourado A (2006) Pruning for interpretability of large spanned eTS. In: Proceedings of the 2006 international symposium on evolving fuzzy systems, EFS'06, IEEE Press, Ambelside, Lake District, UK, pp 55-60.
42. Rong HJ, Sundararajan N, Huang GB, Saratchandran P (2006) Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Syst 157(9):1260-1275.
43. Rong HJ, Huang GB, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern B 39(4):1067-1072.
44. SantaFeLaser (2010) The Santa Fe Time Series Competition Data. Data Set A: Laser generated data. http://www-psych.stanford.edu/*andreas/Time-Series/SantaFe.html.
45. Schölkopf B, Smola AJ (2002) Learning with kernels. Support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge. ISBN: 0262194759.
46. Similä T, Tikka J (2005) Multiresponse sparse regression with application to multidimensional scaling. In: Proceedings of the international conference on artificial neural networks, Warsaw, Poland, vol 3967, pp 97-102.
47. Sorjamaa A, Miche Y, Weiss R, Lendasse A (2008) Long-Term Prediction of Time Series using NNE-based Projection and OP-ELM. In: Proceedings of the international joint conference on neural networks, Hong Kong, China, pp 2675-2681.
48. StatLib (2010) Department of Statistics, Carnegie Mellon University. http://lib.stat.cmu.edu/datasets/.
49. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. World Scientific, Singapore.
50. Tsonis AA, Swanson K, Kravtsov S (2007) A new dynamical mechanism for major climate shifts. Geophys Res Lett 34:L13,705, 5 pp.
51. Van der Linden RAM, the SIDC Team (2008) Online catalogue of the Sunspot Index. RWC Belgium, World Data Center for the Sunspot Index, Royal Observatory of Belgium, years 1748-2007, http://sidc.oma.be/html/sunspot.html.
52. Weigend A, Gershenfeld N (1994) Times series prediction: forecasting the future and understanding the past. Addison-Wesley, Reading.
53. Wu Z, Huang NE, Long SR, Peng CK (2007) On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Nat Acad Sci 104(38):14889-14894.
54. Yager R (2008) Measures of specificity over continuous spaces under similarity relations. Fuzzy Sets Syst 159(17):2193-2210.
55. Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111-127.
56. Zeng XJ, Singh MG (1995) Approximation theory of fuzzy systems-MIMO case. IEEE Trans Fuzzy Syst 3(2):219-235.
57. Zhou SM, Gan JQ (2008) Low-level interpretability and high-level interpretability: a unified view of datadriven interpretable fuzzy system modelling. Fuzzy Sets Syst 159(23):3091-3131.