Evolving granular neural networks from fuzzy data streams

Neural Networks

Volumn 38, Issue , 2013, Pages 1-16

  Leite, Daniel ^a   Costa, Pyramo ^b   Gomide, Fernando ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b PONTIFICAL CATHOLIC UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Evolving systems; Granular computing; Information fusion; Neurofuzzy networks; Online modeling

Indexed keywords

APPLICATION EXAMPLES; DATA STREAM; EVOLVING FUZZY SYSTEMS; EVOLVING SYSTEMS; FUNCTION APPROXIMATION; FUNCTION REPRESENTATIONS; FUZZY DATA; FUZZY INTERVAL; FUZZY MODELING; FUZZY MODELS; LOCAL MODEL; NEURAL NETWORK STRUCTURES; NEURO-FUZZY NETWORK; NONSTATIONARY; ONLINE INCREMENTAL LEARNING; ONLINE MODELING; PARAMETER CHANGES; STATE-OF-THE-ART APPROACH;

BIOMEDICAL ENGINEERING; DATA COMMUNICATION SYSTEMS; GRANULAR COMPUTING; INFORMATION FUSION; LEARNING ALGORITHMS; STRUCTURE (COMPOSITION);

NEURAL NETWORKS;

ACCURACY; ARTICLE; BIOMEDICAL ENGINEERING; CLASSIFICATION; DATA ANALYSIS; EVOLVING GRANULAR NEURAL NETWORK; FUZZY SYSTEM; LEARNING ALGORITHM; LINEAR SYSTEM; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; ONLINE SYSTEM; PRIORITY JOURNAL; PROBABILITY; PROCESS DEVELOPMENT; SIGNAL NOISE RATIO; SIMULATION; UNCERTAINTY;

COMPUTER SIMULATION; DATABASES, FACTUAL; FUZZY LOGIC; HUMANS; NEURAL NETWORKS (COMPUTER); PARKINSON DISEASE;

EID: 84870233667     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.10.006     Document Type: Article

References (62)

1. Aggarwal C.C., Yu P.S. Privacy-preserving data mining: models and algorithms. Series: Advances in database systems 2008, Verlag: Springer.
2. Alonso J.M., Magdalena L. Special issue on interpretable fuzzy systems. Information Sciences 2010, 181:4331-4339.
3. Angelov P. Evolving rule-based models: a tool for design of flexible adaptive systems 2002, Springer, Heidelberg.
4. Angelov P., Filev D. An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics - Part B 2004, 34(1):484-498.
5. Angelov P., Filev D., Kasabov N. Evolving intelligent systems: methodology and applications. Series on computational intelligence 2010, Wiley-IEEE Press, Hoboken.
6. Angelov P., Zhou X. Evolving fuzzy systems from data streams in real-time. IEEE Symposium on Evolving Fuzzy Systems 2006, 29-35.
7. Angelov P., Zhou X. Evolving fuzzy-rule-based classifiers from data streams. IEEE Transactions on Fuzzy Systems 2008, 16(6):1462-1475.
8. Astrom K.J., Wittenmark B. Adaptive control 1994, Addison-Wesley, Boston. 2nd ed.
9. Ballini R., Mendonca A., Gomide F. Evolving fuzzy modeling of sovereign bonds. Journal of Financial Decision Making 2009, 5(2):3-15.
10. Bargiela A., Pedrycz W. Granular computing: an introduction 2002, Kluwer Academic, Boston.
11. Beliakov G., Pradera A., Calvo T. Aggregation functions: a guide for practitioners 2007, Springer, Berlin.
12. Bouchon-Meunier B. Aggregation and fusion of imperfect information 1998, Physica-Verlag, Heidelberg.
13. Carpenter G.A., Grossberg S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing 1987, 37:54-115.
14. Carpenter G.A., Grossberg S., Markuzon N., Reynolds J.H., Rosen D.B. Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks 1992, 3(5):698-713.
15. Dubois D., Prade H. On the use of aggregation operations in information fusion processes. Fuzzy Sets and Systems 2004, 142(1):143-161.
16. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006, 27:861-874.
17. Gabrys B., Bargiela A. General fuzzy min-max neural network for clustering and classification. IEEE Transactions on Neural Networks 2000, 11(3):769-783.
18. Gabrys B., Petrakieva L. Combining labelled and unlabelled data in the design of pattern classification systems. International Journal of Approximate Reasoning 2004, 35(3):251-273.
19. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction 2009, Springer, New York. 2nd ed.
20. Haykin S. Neural networks: a comprehensive foundation 1999, Prentice Hall, Upper Saddle River. 2nd ed.
21. Ho A., Iansek R., Marigliani C., Bradshaw J., Gates S. Speech impairment in a large sample of patients with Parkinson's disease. Behavioral Neurology 1998, 11:131-137.
22. Johnson C.R. Lectures on adaptive parameter estimation 1988, Prentice-Hall, Upper Saddle River.
23. Kaburlasos V.G., Papadakis S.E. Granular self-organizing map (grSOM) for structure identification. Neural Networks 2006, 19(5):623-643.
24. Kasabov N. Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning. IEEE Transactions on Systems, Man, and Cybernetics - Part B 2001, 31(6):902-918.
25. Kasabov N. Evolving connectionist systems: the knowledge engineering approach 2007, Springer, London. 2nd ed.
26. Kasabov N., Song Q. DENFIS: dynamic evolving neural-fuzzy inference system and its application. IEEE Transactions on Fuzzy Systems 2002, 10(2):144-154.
27. Kausay T., Simon T.K. Acceptance of concrete compressive strength. Concrete Structures 2007, 8:54-63.
28. Leite D., Ballini R., Costa P., Gomide F. Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evolving Systems 2012, 3(1):65-79.
29. Leite D., Costa P., Gomide F. Evolving granular neural network for semi-supervised data stream classification. International Joint Conference on Neural Networks 2010, 1-8.
30. Leite D., Costa P., Gomide F. Interval approach for evolving granular system modeling. Learning in non-stationary environments: methods and applications 2012, Springer. M. Mouchaweh, E. Lughofer (Eds.).
31. Leite D., Gomide F. Evolving linguistic fuzzy models from data streams. Combining experimentation and theory: a homage to Abe Mamdani 2011, Springer. E. Trillas (Ed.).
32. Lin T.Y. Granular computing: practices, theories, and future directions. Encyclopedia of complexity and systems science 2009, Springer. R.A. Meyers (Ed.).
33. Little M.A., McSharry P.E., Hunter E.J., Spielman J., Ramig L.O. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Transactions on Biomedical Engineering 2009, 56(4):1015-1022.
34. Lughofer E. On-line incremental feature weighting in evolving fuzzy classifiers. Fuzzy Sets and Systems 2011, 163(1):1-23.
35. Lughofer E., Bouchot J.-L., Shaker A. On-line elimination of local redundancies in evolving fuzzy systems. Evolving Systems 2011, 2(3):165-187.
36. Maimon O.Z., Rokach L. The data mining and knowledge discovery handbook 2005, Springer, New York.
37. Mitchell T.M. The role of unlabeled data in supervised learning. International Colloquium on Cognitive Science 1999, 1-8.
38. Nandedkar A.V., Biswas P.K. A granular reflex fuzzy min-max neural network for classification. IEEE Transactions on Neural Networks 2009, 20(7):1117-1134.
39. Pedrycz W. Heterogeneous fuzzy logic networks: fundamentals and development studies. IEEE Transactions on Neural Networks 2004, 15(6):1466-1481.
40. Pedrycz W. Knowledge-based clustering: from data to information granules 2005, Wiley, Hoboken.
41. Pedrycz W. Evolvable fuzzy systems: some insights and challenges. Evolving Systems 2010, 1(2):73-82.
42. Pedrycz W., Gomide F. Fuzzy systems engineering: toward human-centric computing 2007, Wiley, Hoboken.
43. Pedrycz W., Skowron A., Kreinovich V. Handbook of granular computing 2008, Wiley, Chinchester.
44. Pedrycz W., Vukovich W. Granular neural networks. Neurocomputing 2001, 36:205-224.
45. Shafer J.L. Knowledge-based clustering: from data to information granules 1997, Chapman and Hall, London.
46. Simpson P.K. Fuzzy min-max neural networks. part I: classification. IEEE Transactions on Neural Networks 1992, 3(5):776-786.
47. Simpson P.K. Fuzzy min-max neural networks. part II: clustering. IEEE Transactions on Fuzzy Systems 1993, 1(1):32-45.
48. Tibshirani R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society - Series B 1996, 58(1):267-288.
49. Tsanas A., Little M.A., McSharry P.E., Ramig L.O. Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests. IEEE Transactions on Biomedical Engineering 2010, 57(4):884-893.
50. Witten I.H., Frank E., Hall M.A. Data mining: practical machine learning tools and techniques 2011, Morgan Kaufmann, Burlington.
51. Yager R.R. Learning from imprecise granular data using trapezoidal fuzzy set representations. Lecture notes in computer science 2007, vol. 4772:244-254. H. Prade, V.S. Subrahmanian (Eds.).
52. Yager R.R. Participatory learning with granular observations. IEEE Transactions on Fuzzy Systems 2009, 17(1):1-13.
53. Yao Y.Y. Perspectives of granular computing. IEEE International Conference on Granular Computing 2005, 85-90.
54. Yao Y.Y. Granular computing: past, present and future. IEEE International Conference on Granular Computing 2008, 80-85.
55. Yao Y.Y. Human-inspired granular computing. Novel developments in granular computing: applications for advanced human reasoning and soft computation 2010, IGI Global. J.T. Yao (Ed.).
56. Yeh i.-C. Modeling of strength of high performance concrete using artificial neural networks. Cement and Concrete Research 1998, 28(12):1797-1808.
57. Young P.C. Recursive estimation and time-series analysis: an introduction 1984, Springer, Berlin.
58. Zadeh L.A. Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 1997, 19:111-127.
59. Zadeh L.A. Generalized theory of uncertainty (GTU)-principal concepts and ideas. Computational Statistics & Data Analysis 2006, 51:15-46.
60. Zhang Y.-Q., Fraser M.D., Gagliano R.A., Kandel A. Granular neural networks for numerical-linguistic data fusion and knowledge discovery. IEEE Transactions on Neural Networks 2000, 11(3):658-667.
61. Zhang L., Zhang B. Fuzzy reasoning model under quotient space structure. Information Sciences 2005, 173:353-364.
62. Zhu X., Goldberg A.B. Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning 2009, Morgan and Claypool Publishers.