Detecting and reacting on drifts and shifts in on-line data streams with evolving fuzzy systems

2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference, IFSA-EUSFLAT 2009 - Proceedings

Volumn , Issue , 2009, Pages 931-937

  Lughofer, Edwin ^a   Angelov, Plamen ^b  

^a JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

^b LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Age of a cluster fuzzy rule; Detection and reaction to drifts and shifts; Drifts and shifts in data streams; Evolving fuzzy systems; Gradual forgetting

Indexed keywords

AGE OF A CLUSTER/FUZZY RULE; ALTERNATIVE METHODS; AUTOMATIC ADAPTATION; AUTOMATIC DETECTION; DATA STREAM; DENSITY-BASED CLUSTERING; EMPIRICAL EVALUATIONS; EVOLVING FUZZY SYSTEMS; FUZZY MODELS; GRADUAL FORGETTING; LEARNING RATES; LOCAL LEARNING; ONLINE DATA; REAL WORLD DATA; SUB-PROBLEMS; VECTOR QUANTIZATION APPROACH;

DATA COMMUNICATION SYSTEMS; FUZZY LOGIC; FUZZY SETS; VIRTUAL REALITY;

FUZZY SYSTEMS;

EID: 79951871855     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. P. Angelov and D. Filev. Simpl eTS: A simplified method for learning evolving Takagi-Sugeno fuzzy models. In Proceedings of FUZZ-IEEE 2005, pages 1068-1073, Reno, Nevada, U.S.A., 2005.
2. P. Angelov and X. Zhou. Evolving fuzzy-rule-based classifiers from data streams. IEEE Transactions on Fuzzy Systems, 16(6): 1462-1475, 2008.
3. P.P. Angelov. Evolving Takagi-Sugeno fuzzy systems from streaming data, eTS+. In P. Angelov, D. Filev, and N. Kasabov, editors, Evolving Intelligent Systems: Methodology and Applications, page to appear. John Wiley & Sons, New York, 2008.
4. P.P. Angelov and D. Filev. An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans. on Systems, Man and Cybernetics, part B, 34(1): 484-498, 2004.
5. P.P. Angelov, E. Lughofer, and X. Zhou. Evolving fuzzy classifiers using different model architectures. Fuzzy Sets and Systems, 159(23): 3160-3182, 2008.
6. P.P. Angelov and X.-W. Zhou. Evolving fuzzy systems from data streams in real-time. In 2006 International Symposium on Evolving Fuzzy Systems, pages 29-35, 2006.
7. J. Beringer and E. Hüllermeier. Efficient instance-based learning on data streams. Intelligent Data Analysis, 11(6): 627-650, 2007.
8. F. Hopner and F. Klawonn. Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression. Proc. 4th Intern. Conf. on Knowledge-based Intell. Eng. Syst. (KES), Brighton, UK, pages pp. 162-165, 2000.
9. P. Angelov. Machine Learning (Collaborative Systems), patent (WO2008053161, priority date: 1 November 2006).
10. P. Angelov and X.-W. Zhou. On Line Learning Fuzzy Rule-based System Structure from Data Streams. In 2008 IEEE International Conference on Fuzzy Systems within the IEEE World Congress on Computational Intelligence, Hong Kong, June 1-6, 2008, pages 915-922, 2008.
11. P.P. Angelov Evolving Rule-based Models: A Tool for Flexible Systems Design, Springer Physica Verlag, Heidelberg, Germany, February, 2002.
12. N. Sundararajan H.-J. Rong, G.-B. Huang, and P. Saratchandran. Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets and Syst., 157(9): 1260-1275, 2006.
13. N. K. Kasabov and Q. Song. DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. on Fuzzy Systems, 10(2): 144-154, 2002.
14. R. Klinkenberg. Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis, Vol. 8 (3), pages 281-300, 2004.
15. G. Leng, T.M McGinnity, and G. Prasad. An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets and Syst., 150(2): 211-243, 2005.
16. E. Lima, F. Gomide, and R. Ballini. Participatory Evolving Fuzzy Modeling. In P. Angelov, D. Filev, and N. Kasabov, editors, 2006 International Symposium on Evolving Fuzzy Systems, IEEE Press, pages 36-41, 2006.
17. L. Ljung. System Identification: Theory for the User. Prentice Hall PTR, Prentic Hall Inc., Upper Saddle River, New Jersey 07458, 1999.
18. E. Lughofer. Evolving Fuzzy Models - Incremental Learning, Interpretability and Stability Issues, Applications. VDM Verlag Dr. Müller, Saarbrücken, 2008.
19. E. Lughofer. Extensions of vector quantization for incremental clustering. Pattern Recognition, 41(3): 995-1011, 2008.
20. E. Lughofer. FLEXFIS: A robust incremental learning approach for evolving TS fuzzy models. IEEE Trans. on Fuzzy Systems (sp. issue on EFS), 16(6): 1393-1410, 2008.
21. E. Lughofer. Towards robust evolving fuzzy systems. In P. Angelov, D. Filev, and N. Kasabov, editors, Evolving Intelligent Systems: Methodology and Applications, to appear. John Wiley & Sons, New York, 2009.
22. E. Lughofer, P. Angelov, and X. Zhou. Evolving singleand multi-model fuzzy classifiers with FLEXFIS-Class. In Proc. FUZZ-IEEE 2007, pages 363-368, London, UK, 2007.
23. E. Lughofer, J. E. Smith, M. A. Tahir, P. Caleb-Solly, C. Eitzinger, D. Sannen, and M. Nuttin. Human-Machine Interaction Issues in Quality Control Based on On-Line Image Classification. IEEE Trans. on Systems, Man and Cybernetics part A: Systems and Humans, 2009, to appear.
24. S. Ramamurthy and R. Bhatnagar. Tracking recurrent concept drift in streaming data using ensemble classifiers. In Proc. 6th Intern. Conf. on Machine Learning and Applic. (ICMLA), 2007, pages 404-409, 2007.
25. T. Takagi and M. Sugeno. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. on Systems, Man and Cybernetics, 15(1): 116-132, 1985.
26. A. Tsymbal. The problem of concept drift: definitions and related work. Technical Report TCD-CS-2004-15, Dept of Comp Science, Trinity College Dublin, Ireland, 2004.
27. L.X. Wang. Fuzzy systems are universal approximators. In Proc. 1st IEEE Conf. Fuzzy Systems, pages 1163-1169, San Diego, CA, 1992.
28. S. Wu, M.J. Er, and Y. Gao. A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans. on Fuzzy Systems, 9(4): 578-594, 2001.