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Learning Drifting Concepts

Learning Drifting Concepts

Projects

SFB 531 Computational Intelligence

Software

RapidMiner (YALE)
RapidMiner Data Stream Plugin (formerly: YALE Concept Drift Plugin)

Staff

Bockermann, Christian
Klinkenberg, Ralf

Master Thesis Proposals

Past Master Thesis

Publications

Scholz/Klinkenberg/2006b Scholz, Martin and Klinkenberg, Ralf. Boosting Classifiers for Drifting Concepts. In Intelligent Data Analysis (IDA), Special Issue on Knowledge Discovery from Data Streams, Vol. 11, No. 1, pages 3--28, 2007.
Mierswa/etal/2006a Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm. YALE: Rapid Prototyping for Complex Data Mining Tasks. In Tina Eliassi-Rad and Lyle H. Ungar and Mark Craven and Dimitrios Gunopulos (editors), Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pages 935--940, ACM, New York, USA, ACM Press, 2006.
Scholz/Klinkenberg/2006a Scholz, Martin and Klinkenberg, Ralf. Boosting Classifiers for Drifting Concepts. No. 6/06, Collaborative Research Center on the Reduction of Complexity for Multivariate Data Structures (SFB 475), University of Dortmund, Dortmund, Germany, 2006.
Klinkenberg/2005a Klinkenberg, Ralf. Meta-Learning, Model Selection, and Example Selection in Machine Learning Domains with Concept Drift. In Furnkranz, Johannes and Grieser, Gunter (editors), Annual workshop of the special interest group on machine learning, knowledge discovery, and data mining (FGML-2005) of the German Computer Science Society (GI) within the workshop week \em Learning -- Knowledge Discovery -- Adaptivity (LWA-2005), Saarbrucken, Germany, 2005.
Scholz/Klinkenberg/2005a Scholz, Martin and Klinkenberg, Ralf. An Ensemble Classifier for Drifting Concepts. In Gama, J. and Aguilar-Ruiz, J. S. (editors), Proceedings of the Second International Workshop on Knowledge Discovery in Data Streams, pages 53--64, Porto, Portugal, 2005.
Klinkenberg/2004a Klinkenberg, Ralf. Learning Drifting Concepts: Example Selection vs. Example Weighting. In Intelligent Data Analysis (IDA), Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift, Vol. 8, No. 3, pages 281--300, 2004.
Klinkenberg/2003a Klinkenberg, Ralf. Predicting Phases in Business Cycles Under Concept Drift. In Hotho, Andreas and Stumme, Gerd (editors), LLWA 2003 -- Tagungsband der GI-Workshop-Woche \em Lehren -- Lernen -- Wissen -- Adaptivitat, Proceedings of the Workshop Week \em Teaching -- Learning -- Knowledge -- Adaptivity of the National German Computer Science Society (GI) / Annual Workshop on Machine Learning, pages 3--10, Karlsruhe, Germany, 2003.
Klinkenberg/Rueping/2003a Klinkenberg, Ralf and Rüping, Stefan. Concept Drift and the Importance of Examples. In Franke, Jurgen and Nakhaeizadeh, Gholamreza and Renz, Ingrid (editors), Text Mining -- Theoretical Aspects and Applications, pages 55--77, Berlin, Germany, Physica-Verlag, 2003.
Daniel/etal/2002a Daniel, Guido and Dienstuhl, J. and Engell, S. and Felske, S. and Goser, K. and Klinkenberg, R. and Morik, K. and Ritthoff, O. and Schmidt-Traub, H.. Novel Learning Tasks, Optimization, and Their Application. In Schwefel, H.-P. and Wegener, I. and Weinert, K. (editors), Advances in Computational Intelligence -- Theory and Practice, pages 245--318, Berlin, Germany, Springer, 2002.
Klinkenberg/2002a Klinkenberg, Ralf. Transductive Learning of Drifting Concepts. No. CI-125/02, Collaborative Research Center 531, University of Dortmund, Dortmund, Germany, 2002.
Klinkenberg/etal/2002a Klinkenberg, Ralf and Ritthoff, Oliver and Morik, Katharina. Novel Learning Tasks From Practical Applications. In Henze, Nicola and Kókai, Gabriella and Zeidler, Jens (editors), LLA'02: Lehren -- Lernen -- Adaptivitat, Proceedings of the workshop of the special interest groups Machine Learning (FGML), Intelligent Tutoring Systems (ILLS), and Adaptivity and User Modeling in Interactive Systems (ABIS) of the German Computer Science Society (GI), pages 46--59, Hannover, Germany, University of Hannover, 2002.
Klinkenberg/2001a Klinkenberg, Ralf. Using Labeled and Unlabeled Data to Learn Drifting Concepts. In Kubat, Miroslav and Morik, Katharina (editors), Workshop notes of the IJCAI-01 Workshop on \em Learning from Temporal and Spatial Data, pages 16--24, IJCAI, Menlo Park, CA, USA, AAAI Press, 2001.
Klinkenberg/Joachims/2000a Klinkenberg, Ralf and Joachims, Thorsten. Detecting Concept Drift with Support Vector Machines. In Langley, Pat (editors), Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pages 487--494, San Francisco, CA, USA, Morgan Kaufmann, 2000.
Klinkenberg/99a Klinkenberg, Ralf. Learning Drifting Concepts with Partial User Feedback. In Perner, Petra and Fink, Volkmar (editors), Beitrage zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen (FGML-99), Magdeburg, Germany, 1999.
Klinkenberg/98a Klinkenberg, Ralf. Maschinelle Lernverfahren zum adaptiven Informationsfiltern bei sich verandernden Konzepten. Fachbereich Informatik, Universitat Dortmund, Germany, 1998.
Klinkenberg/Renz/98a Klinkenberg, Ralf and Renz, Ingrid. Adaptive Information Filtering: Learning in the Presence of Concept Drifts. In Sahami, Mehran and Craven, Mark and Joachims, Thorsten and McCallum, Andrew (editors), Workshop Notes of the ICML/AAAI-98 Workshop \em Learning for Text Categorization, pages 33--40, Menlo Park, CA, USA, AAAI Press, 1998.
Klinkenberg/Renz/98b Klinkenberg, Ralf and Renz, Ingrid. Adaptive Information Filtering: Learning Drifting Concepts. In Wysotzki, F. and Geibel, P. and Schadler, K. (editors), Beitrage zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen (FGML-98), No. 98/11, pages 98--105, Germany, Fachbereich Informatik, TU Berlin, 1998.