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Erschienen in:
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2012 | OriginalPaper | Buchkapitel

1. Prologue

verfasst von : Moamar Sayed-Mouchaweh, Edwin Lughofer

Erschienen in: Learning in Non-Stationary Environments

Verlag: Springer New York

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Abstract

This introductory chapter intends to provide a general overview about the most essential requirements, demands and challenges with respect to dynamic learning of data-driven models in non-stationary environments and applications. It outlines the main lines of research investigated during the last decade in order to cope with the requirements, inter alia to handle high system dynamics, online data streams recorded with a high frequency, drifting system states and very large data bases within fast sample-wise and single-pass model updates conducted on-the-fly and in incremental manner. The last part of this chapter outlines a compact summary of the contents of the book by providing a paragraph about each of the single contributions.

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Literatur
1.
Zurück zum Zitat Abraham, A., Dote, Y.: Engineering Hybrid Soft Computing Systems. Springer, New York (2010) Abraham, A., Dote, Y.: Engineering Hybrid Soft Computing Systems. Springer, New York (2010)
2.
Zurück zum Zitat Angelov, P., Filev, D.: An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Transactions on Systems, Man and Cybernetics, part B: Cybernetics 34(1), 484–498 (2004)CrossRef Angelov, P., Filev, D.: An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Transactions on Systems, Man and Cybernetics, part B: Cybernetics 34(1), 484–498 (2004)CrossRef
3.
Zurück zum Zitat Angelov, P., Filev, D., Kasabov, N.: Evolving Intelligent Systems—Methodology and Applications. John Wiley & Sons, New York (2010)CrossRef Angelov, P., Filev, D., Kasabov, N.: Evolving Intelligent Systems—Methodology and Applications. John Wiley & Sons, New York (2010)CrossRef
4.
Zurück zum Zitat Angelov, P., Kasabov, N.: Evolving computational intelligence systems. In: Proceedings of the 1st International Workshop on Genetic Fuzzy Systems, pp. 76–82. Granada, Spain (2005) Angelov, P., Kasabov, N.: Evolving computational intelligence systems. In: Proceedings of the 1st International Workshop on Genetic Fuzzy Systems, pp. 76–82. Granada, Spain (2005)
5.
Zurück zum Zitat Angstenberger, L.: Dynamic fuzzy pattern recognition. Ph.D. thesis, Fakultät für Wirtschaftswissenschaften der Rheinisch-Westfälischen Technischen Hochschule (2000). Aachen, Germany Angstenberger, L.: Dynamic fuzzy pattern recognition. Ph.D. thesis, Fakultät für Wirtschaftswissenschaften der Rheinisch-Westfälischen Technischen Hochschule (2000). Aachen, Germany
6.
Zurück zum Zitat Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the SIAM International Conference on Data Mining, pp. 443–448. Minneapolis, MN (2007) Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the SIAM International Conference on Data Mining, pp. 443–448. Minneapolis, MN (2007)
7.
Zurück zum Zitat Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: Massive online analysis. Journal of Machine Learning Research 11, 1601–1604 (2010) Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: Massive online analysis. Journal of Machine Learning Research 11, 1601–1604 (2010)
8.
Zurück zum Zitat Boubacar, A., Lecoeuche, H., Maouche, S.: Audyc neural network using a new gaussian densities merge mechanism. In: 7th International Conference on Adaptive and Natural Computing Algorithms, pp. 155–158. Coimbra, Portugal (2005) Boubacar, A., Lecoeuche, H., Maouche, S.: Audyc neural network using a new gaussian densities merge mechanism. In: 7th International Conference on Adaptive and Natural Computing Algorithms, pp. 155–158. Coimbra, Portugal (2005)
9.
Zurück zum Zitat Bouchachia, A.: Evolving clustering: an asset for evolving systems. IEEE SMC Newsletter 36 (2011) Bouchachia, A.: Evolving clustering: an asset for evolving systems. IEEE SMC Newsletter 36 (2011)
10.
Zurück zum Zitat Diehl, C., Cauwenberghs, G.: SVM incremental learning, adaptation and optimization. In: Proceedings of the International Joint Conference on Neural Networks Vol. 4, pp. 2685–2690. Boston (2003) Diehl, C., Cauwenberghs, G.: SVM incremental learning, adaptation and optimization. In: Proceedings of the International Joint Conference on Neural Networks Vol. 4, pp. 2685–2690. Boston (2003)
11.
Zurück zum Zitat Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80. Boston, MA (2000) Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80. Boston, MA (2000)
12.
Zurück zum Zitat Gama, J.: Knowledge Discovery from Data Streams. Chapman & Hall/CRC, Boca Raton, Florida (2010)MATHCrossRef Gama, J.: Knowledge Discovery from Data Streams. Chapman & Hall/CRC, Boca Raton, Florida (2010)MATHCrossRef
13.
Zurück zum Zitat Hartert, L., Sayed-Mouchaweh, M., Billaudel, P.: A semi-supervised dynamic version of Fuzzy K-Nearest Neighbours to monitor evolving systems. Evolving Systems, 1 (1), 3–15 (2010)CrossRef Hartert, L., Sayed-Mouchaweh, M., Billaudel, P.: A semi-supervised dynamic version of Fuzzy K-Nearest Neighbours to monitor evolving systems. Evolving Systems, 1 (1), 3–15 (2010)CrossRef
14.
Zurück zum Zitat Haykin, S.: Neural Networks: A Comprehensive Foundation (2nd Edition). Prentice Hall Inc., Upper Saddle River, New Jersey (1999)MATH Haykin, S.: Neural Networks: A Comprehensive Foundation (2nd Edition). Prentice Hall Inc., Upper Saddle River, New Jersey (1999)MATH
15.
Zurück zum Zitat Huang, G., Saratchandran, P., Sundararajan, N.: A generalized growing and pruning rbf (ggap-rbf) neural network for function approximation. IEEE Transactions on Neural Networks 16(1), 57–67 (2005)CrossRef Huang, G., Saratchandran, P., Sundararajan, N.: A generalized growing and pruning rbf (ggap-rbf) neural network for function approximation. IEEE Transactions on Neural Networks 16(1), 57–67 (2005)CrossRef
16.
Zurück zum Zitat Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach—Second Edition. Springer Verlag, London (2007) Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach—Second Edition. Springer Verlag, London (2007)
17.
Zurück zum Zitat Kruse, R., Gebhardt, J., Palm, R.: Fuzzy Systems in Computer Science. Verlag Vieweg, Wiesbaden (1994)CrossRef Kruse, R., Gebhardt, J., Palm, R.: Fuzzy Systems in Computer Science. Verlag Vieweg, Wiesbaden (1994)CrossRef
18.
Zurück zum Zitat Kuncheva, L.: Classifier ensembles for changing environments. In: Proceedings of the 5th International Workshop in Multiple Classifier Systems, pp. 1–15. Cagliari, Italy (2004) Kuncheva, L.: Classifier ensembles for changing environments. In: Proceedings of the 5th International Workshop in Multiple Classifier Systems, pp. 1–15. Cagliari, Italy (2004)
19.
Zurück zum Zitat Lecoeuche, S., Lurette, C.: Auto-adaptive and dynamical clustering neural network. In: Proceedings of the International Conference on Artificial Neural Networks, pp. 350–358. Istanbul, Turkey (2003) Lecoeuche, S., Lurette, C.: Auto-adaptive and dynamical clustering neural network. In: Proceedings of the International Conference on Artificial Neural Networks, pp. 350–358. Istanbul, Turkey (2003)
20.
Zurück zum Zitat Ljung, L.: System Identification: Theory for the User. Prentice Hall PTR, Prentic Hall Inc., Upper Saddle River, New Jersey (1999) Ljung, L.: System Identification: Theory for the User. Prentice Hall PTR, Prentic Hall Inc., Upper Saddle River, New Jersey (1999)
21.
Zurück zum Zitat Lughofer, E.: Evolving Fuzzy Systems—Methodologies, Advanced Concepts and Applications. Springer, Berlin Heidelberg (2011)MATHCrossRef Lughofer, E.: Evolving Fuzzy Systems—Methodologies, Advanced Concepts and Applications. Springer, Berlin Heidelberg (2011)MATHCrossRef
22.
Zurück zum Zitat Lughofer, E., Bouchot, J.L., Shaker, A.: On-line elimination of local redundancies in evolving fuzzy systems. Evolving Systems 2(3), 165–187 (2011)CrossRef Lughofer, E., Bouchot, J.L., Shaker, A.: On-line elimination of local redundancies in evolving fuzzy systems. Evolving Systems 2(3), 165–187 (2011)CrossRef
23.
Zurück zum Zitat Lughofer, E.: Human-inspired evolving machines—the next generation of evolving intelligent systems? IEEE SMC newsletter 36 (2011) Lughofer, E.: Human-inspired evolving machines—the next generation of evolving intelligent systems? IEEE SMC newsletter 36 (2011)
24.
Zurück zum Zitat Minku, F., White, A., Yao, X.: The impact of diversity on on-line ensemble learning in the presence of concept drift. IEEE Transactions on Knowledge and Data Engineering 22, 730–742 (2010)CrossRef Minku, F., White, A., Yao, X.: The impact of diversity on on-line ensemble learning in the presence of concept drift. IEEE Transactions on Knowledge and Data Engineering 22, 730–742 (2010)CrossRef
25.
Zurück zum Zitat Mouss, H., Mouss, D., Mouss, N., Sefouhi, L.: Test of Page-Hinkley, an approach for fault detection in an agro-alimentary production system. In: Proceedings of the Asian Control Conference, Volume 2, pp. 815–818 (2004) Mouss, H., Mouss, D., Mouss, N., Sefouhi, L.: Test of Page-Hinkley, an approach for fault detection in an agro-alimentary production system. In: Proceedings of the Asian Control Conference, Volume 2, pp. 815–818 (2004)
26.
Zurück zum Zitat Nakhaeizadeh, G., Taylor, C., Kunisch, G.: Dynamic supervised learning. Some basic issues and application aspects. Classification and knowledge organization. pp. 123–135. Springer Verlag, Berlin Heidelberg (1997) Nakhaeizadeh, G., Taylor, C., Kunisch, G.: Dynamic supervised learning. Some basic issues and application aspects. Classification and knowledge organization. pp. 123–135. Springer Verlag, Berlin Heidelberg (1997)
27.
Zurück zum Zitat Nelles, O.: Nonlinear System Identification. Springer, Berlin (2001)MATH Nelles, O.: Nonlinear System Identification. Springer, Berlin (2001)MATH
28.
Zurück zum Zitat Oza, N.C., Russell, S.: Online bagging and boosting. Artificial Intelligence and Statistics, pp. 105–112 (2001) Oza, N.C., Russell, S.: Online bagging and boosting. Artificial Intelligence and Statistics, pp. 105–112 (2001)
29.
Zurück zum Zitat Pedrycz, W., Gomide, F.: Fuzzy Systems Engineering: Toward Human-Centric Computing. John Wiley & Sons, Hoboken, New Jersey (2007) Pedrycz, W., Gomide, F.: Fuzzy Systems Engineering: Toward Human-Centric Computing. John Wiley & Sons, Hoboken, New Jersey (2007)
30.
Zurück zum Zitat Pedrycz, W., Rai, P.: Collaborative clustering with the use of fuzzy c-means and its quantification. Fuzzy Sets and Systems 159(18), 2399–2427 (2008)MathSciNetMATHCrossRef Pedrycz, W., Rai, P.: Collaborative clustering with the use of fuzzy c-means and its quantification. Fuzzy Sets and Systems 159(18), 2399–2427 (2008)MathSciNetMATHCrossRef
31.
Zurück zum Zitat Pedrycz, W., Weber, R.: Special issue on soft computing for dynamic data mining. Applied Soft Computing 8(4), 1281–1282 (2008)CrossRef Pedrycz, W., Weber, R.: Special issue on soft computing for dynamic data mining. Applied Soft Computing 8(4), 1281–1282 (2008)CrossRef
32.
Zurück zum Zitat Sayed-Mouchaweh, M.: Semi Supervised Classification Method for Dynamic Applications. Fuzzy Sets and Systems, 161(4), 544–563 (2010)MathSciNetCrossRef Sayed-Mouchaweh, M.: Semi Supervised Classification Method for Dynamic Applications. Fuzzy Sets and Systems, 161(4), 544–563 (2010)MathSciNetCrossRef
33.
Zurück zum Zitat Sayed-Mouchaweh, M., Messai, N.: A clustering-based approach for the identification of a class of temporally switched linear systems. Elsevier, Pattern Recognition Letters (PRL), 33(2), 144–151 (2012) Sayed-Mouchaweh, M., Messai, N.: A clustering-based approach for the identification of a class of temporally switched linear systems. Elsevier, Pattern Recognition Letters (PRL), 33(2), 144–151 (2012)
34.
Zurück zum Zitat Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Handling local concept drift with dynamic integration of classifiers: domain of antibiotic resistance in nosocomial infections. In: Proc. 19th IEEE Int. Symposium on Computer-Based Medical Systems CBMS 2006, pp. 679–684. Maribor, Slovenia (2006) Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Handling local concept drift with dynamic integration of classifiers: domain of antibiotic resistance in nosocomial infections. In: Proc. 19th IEEE Int. Symposium on Computer-Based Medical Systems CBMS 2006, pp. 679–684. Maribor, Slovenia (2006)
35.
Zurück zum Zitat Weber, R.: Dynamic data mining. Encyclopedia of Data Warehousing and Mining pp. 722–728 (2009) Weber, R.: Dynamic data mining. Encyclopedia of Data Warehousing and Mining pp. 722–728 (2009)
Metadaten
Titel
Prologue
verfasst von
Moamar Sayed-Mouchaweh
Edwin Lughofer
Copyright-Jahr
2012
Verlag
Springer New York
DOI
https://doi.org/10.1007/978-1-4419-8020-5_1

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