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Incremental Rule-Based Learners for Handling Concept Drift: An Overview Cover

Incremental Rule-Based Learners for Handling Concept Drift: An Overview

Open Access
|Feb 2013

References

  1. [1] An A., Learning Classification Rules from Data, Computers and Mathematics with Applications, vol. 45, p. 737-748, 2003.10.1016/S0898-1221(03)00034-8
  2. [2] Baena-Garcia M., Del Campo-Avila J., Fidalgo R., Bifet A., Early Drift Detection Method, Proceedings of the 4th ECML PKDD International Workshop on Knowledge Discovery from Data Streams, p. 77-86, Berlin, Germany, 2006.
  3. [3] Bakker J., Pechenizkiy M., Food Wholesales Prediction: What is Your Baseline?, Proceedings of the 18th Symposium on Methodologies for Intelligent Systems, ISMIS 2009, Prague, Czech Republic, LNCS, vol. 5722, p. 493-502, 2009.
  4. [4] Bifet A., Holmes G., Pfahringer B., Kranen P., Kremer H., Jansen T., Seidl T.: MOA: Massive Online Analysis a Framework for Stream Classification and Clustering, Workshop on Applications of Pattern Analysis, HaCDAIS, 2010.
  5. [5] Błaszczyński J., Stefanowski J., Zaja̧c M., Ensembles of Abstaining Classifiers Based on Rule Sets, Proceedings of the 18th International Symposium on Methodologies for Intelligent Systems, ISMIS 2009, Prague, Czech Republic, LNCS, vol. 5722, p. 382-391, 2009.
  6. [6] Cendrowska J., PRISM An Algorithm for Inducing Modular Rules, International Journal Man-Machine Studies, vol. 27, p. 349-370, 1987.10.1016/S0020-7373(87)80003-2
  7. [7] Cestnik B., Estimating Probabilities: A Crucial Task in Machine Learning, Pro- ceedings ECAO 1990, Stockholm, Sweden, 1990.
  8. [8] Clark P, Boswell R., Rule Induction with CN2: some recent improvement, Pro- ceedings of 5th European Working Session on Learning, ESWL 1991, Porto, Portugal, p. 151-163, 1991.10.1007/BFb0017011
  9. [9] Clark P., Niblett T., The CN2 Induction Algorithm, Machine Learning, vol. 3, p. 261-283, 1989.10.1007/BF00116835
  10. [10] Deckert M., Batch Weighted Ensemble for Mining Data Streams with Concept Drift, Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems, ISMIS 2011, Warsaw, Poland, LNCS, vol. 6804, p. 290-299, 2011.
  11. [11] Deckert M., Stefanowski J., Comparing Block Ensembles for Data Streams with Concept Drift, Proc. of Workshop Mining Complex and Stream Data, ADBIS 2012, Poznań, Poland, AISC, vol. 185, p. 69-78, 2012.10.1007/978-3-642-32518-2_7
  12. [12] Domingos P., Hulten G., Mining High-Speed Data Streams, Proceedings of the KDD 2000, ACM Press, p. 71-80, 2000.10.1145/347090.347107
  13. [13] Ferrer-Troyano F.J., Aguilar-Ruiz J.A., Riquelme J.C., Incremental Rule Learning and Border Examples Selection from Numerical Data Streams, Journal of Universal Computer Science, vol. 11(8), p. 1426-1439, 2005.
  14. [14] Ferrer-Troyano F.J., Aguilar-Ruiz J.A., Riquelme J.C., Data Streams Classification by Incremental Rule Learning with Parametrized Generalization, Proceed- ings of ACM Symposium on Applied Computing 2006, SAC 2006, p. 657-661, ACM, 2006.10.1145/1141277.1141428
  15. [15] Fürnkranz J., Separate-and-Conquer Rule Learning, Artificial Intelligence Re- view, vol. 13, p.3-54, 1999.10.1023/A:1006524209794
  16. [16] Fürnkranz J., Gamberger D., Lavrač N., Foundations of Rule Learning, Cognitive Technologies, 2012.10.1007/978-3-540-75197-7
  17. [17] Gama J., Medas P., Castillo G., Rodrigues P., Learning with Drift Detection, Proceedings of Brazilian Symposium on Artificial Intelligence, SBIA 2004, LNAI, vol. 3171, p. 286-295, Springer-Verlag, 2004.
  18. [18] Gama J., Knowledge Discovery from Data Streams, Chapman and Hall/CRC 2010.10.1201/EBK1439826119
  19. [19] Gama J., Kosina P., earning Decision Rules from Data Streams, Proceedings of 22th International Joint Conference on Artificial Intelligence, IJCAI 11, vol. 2, p. 1255-1260, AAAI Press, 2011.
  20. [20] Giraud-Carrier C., A Note on the Utility of Incremental Learning, AI Commu- nications, vol. 13, p. 215-223, 2000.
  21. [21] Greco S., S lowiński R., Stefanowski J., Żurawski M., Incremental versus Nonincremental Rule Induction for Multicriteria Classification, Transactions on Rough Sets II, LNCS, vol. 3135, p. 33-53, 2004.
  22. [22] Grzymala-Busse J.W., LERS - A System for Learning from Examples Based on Rough Sets, Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, p. 3-18, 1992.10.1007/978-94-015-7975-9_1
  23. [23] Grzymala-Busse J.W., Selected Algorithms of Machine Learning from Examples, Fundamenta Informaticae, vol. 18, p. 193-207, 1993.10.3233/FI-1993-182-408
  24. [24] Grzymala-Busse J.W., Managing Uncertainty in Machine Learning from Examples. Proceedings of 3rd International Symposium in Intelligent Systems, p. 70-84, 1994.
  25. [25] Hulten G., Spencer L., Domingos P., Mining Time-changing Data Streams, Pro- ceedings of the KDD 2001, ACM Press, p. 97-106, 2001.10.1145/502512.502529
  26. [26] Kosina P., Gama J., Very Fast Decision Rules for Multi-class Problems, Proceed- ings of the 2012 ACM Symposium on Applied Computing, New York, USA, p. 795-800, 2012.10.1145/2245276.2245431
  27. [27] Kosina P., Gama J., Handling Time Changing Data with Adaptive Very Fast Decision Rules, Proceedings of the 2012 European conference on Machine Learn- ing and Knowledge Discovery in Databases, ECML/PKDD 2012, Bristol, United Kingdom, vol. 1, p. 827-842, 2012.10.1007/978-3-642-33460-3_58
  28. [28] Kuncheva L. I., Classifier Ensembles for Changing Environments, Proceedings of 5th International Workshop on Multiple Classifier Systems, MCS 04, LNCS, vol. 3077, p. 1-15, Springer-Verlag, 2004.
  29. [29] Kuncheva L. I., Classifier Ensembles for Detecting Concept Change in Streaming Data: Overview and Perspectives, Proceedings 2nd Workshop SUEMA 2008, ECAI 2008, p. 5-10, Patras, Greece, 2008.
  30. [30] Maison R., Zakrzewicz M., Content-based Load Shedding in Multimedia Data Stream Management System, Foundations of Computing and Decision Sciences, vol. 37(2), p. 79-95, 2012.10.2478/v10209-011-0007-8
  31. [31] Maloof M., Michalski R., Selecting Examples for Partial Memory Learning, Ma- chine Learning, vol. 41, p. 27-52, Kluwer Academic Publishers, 2000.10.1023/A:1007661119649
  32. [32] Maloof M., Michalski R., Incremental Learning with Partial Instance Memory, Artificial Intelligence, vol. 154, p. 95-126, Elsevier, 2003.10.1016/j.artint.2003.04.001
  33. [33] Maloof M., Incremental Rule Learning with Partial Instance Memory for Changing Concepts, Proceedings of the International Joint Conference on Neural Net- works 2003, IJCNN-03, vol. 4, p. 2764-2769, IEEE Press, 2003.
  34. [34] Michalski R.S., A Theory and Methodology of Inductive Learning, Machine Learning: An Artificial Intelligence Approach, p. 83-134, 1983. 10.1016/B978-0-08-051054-5.50008-X
  35. [35] Michalski R.S., Mozetic I., Hong J., Lavrac N., The AQ15 Inductive Learning System: An Overview and Experiments, Report 1260, Department of Computer Science, University of Illinois, 1986.
  36. [36] Nishida K., Yamauchi K., Omori T., ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments, Multiple Classifier Systems, LNCS, vol. 3541, p. 176-185, 2005.
  37. [37] Schlimmer J., Granger R., Incremental Learning from Noisy Data, Machine Learning, vol. 1(3), p. 317-357, 1986.10.1007/BF00116895
  38. [38] Shannon C.E., A Mathematical Theory of Communication, Bell System Technical Journal, vol. 27(3), p. 379-423, 1948.10.1002/j.1538-7305.1948.tb01338.x
  39. [39] Stefanowski J., The Rough Set Based Rule Induction Technique for Classification Problems. Proceedings of the 6th European Conference on Intelligent Techniques and Soft Computing, EUFIT-98, p. 109-113, 1998.
  40. [40] Stefanowski J., Algorytmy Indukcji Regu l Decyzyjnych w Odkrywaniu Wiedzy [in Polish], Habilitation thesis, Rozprawy series, vol. 361, Poznań University of Technology, 2001.
  41. [41] Sulzmann J.N., Fürnkranz J., A Study of Probability Estimation Techniques for Rule Learning, From Local Patterns to Global Models. Proceedings of the ECML/PKDD 2009 Workshop, p. 123-138, 2009.
  42. [42] Tsymbal A., The Problem of Concept Drift: Definitions and RelatedWork, Technical Report, Department of Computer Science, Trinity College Dublin, Ireland, 2004.
  43. [43] Wang H., Fan W., Yu P.S. and Han J., Mining Concept-drifting Data Streams Using Ensemble Classifiers, Proceedings ACM SIGKDD, p. 226-235, 2003.10.1145/956750.956778
  44. [44] Widmer G., Kubat M., Learning in the Presence of Concept Drift and Hidden Contexts, Machine Learning, vol. 23, p. 69-101, 1996.10.1007/BF00116900
  45. [45] Zliobaite I., Learning Under Concept Drift: An Overview, Technical Report, Faculty of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania, 2009.
  46. [46] Zliobaite I., Bakker J., Pechenizkiy M., OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers, Discovery Science, p. 272-286, 2009.10.1007/978-3-642-04747-3_22
  47. [47] Zliobaite I., Bakker J., Pechenizkiy M., Towards Context Aware Food Sales Prediction. In Proceedings of the 3nd International Workshop on Domain Driven Data Mining (DDDM'09), IEEE International Conference on Data Mining ICDM'09, Miami, Florida, USA, p. 94-99, 2009. 10.1109/ICDMW.2009.60
DOI: https://doi.org/10.2478/v10209-011-0020-y | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Language: English
Page range: 35 - 65
Published on: Feb 23, 2013
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2013 Magdalena Deckert, published by Poznan University of Technology
This work is licensed under the Creative Commons License.