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RapidMiner (YALE) - Free Open-Source Java Data Mining

Description:

RapidMiner (formerly YALE) is a freely available open-source environment for machine learning, data mining, and knowledge discovery. Experiments and applications can be made up of a large number of arbitrarily nestable operators and their setup is described by XML files which can easily created with a graphical user interface. Applications of YALE cover both research and real-world data mining tasks.

The set of operators in YALE includes:

  • Machine learning algorithms: support vector machines for regression and classification, decision tree learners (C4.5 and others), clustering algorithms, and a wrapper to all Weka classifiers (learners) and clusterers.
  • Feature operators: selection algoithms like forward selection, backward elimination, and several genetic algorithms, operators for feature extraction from time series, feature weighting and generation.
  • Data preprocessing.
  • Performance evaluation: cross-validation and other evaluation schemes, several performance criteria for classification and regression, operators for parameter optimization in enclosed operators or operator chains, and operators for logging and presenting results.
  • In- and output: flexible operators for data in- and output, support of flexible experimental (re)arrangements, usage of (optional) meta information on data.

YALE provides an easy to use extension mechanism that makes it possible to integrate new operators and adapt YALE to your personal requirements. Since YALE is entirely written in Java, it runs on any major platform/operating system. A command line version allows invoking of Yale from your programs without using the GUI.

Link:

http://www.rapidminer.com/

Software File:

Authors:

Fischer, Simon
Klinkenberg, Ralf
Mierswa, Ingo

Projects:

SFB 475 subproject A4
SFB 531 Computational Intelligence

Publications:

Ritthoff/etal/2002b Ritthoff, Oliver and Klinkenberg, Ralf and Fischer, Simon and Mierswa, Ingo. A Hybrid Approach to Feature Selection and Generation Using an Evolutionary Algorithm. In Bullinaria, John A. (editors), Proceedings of the 2002 U.K. Workshop on Computational Intelligence (UKCI-02), pages 147--154, Birmingham, UK, University of Birmingham, 2002.


Ritthoff/etal/2002a Ritthoff, Oliver and Klinkenberg, Ralf and Fischer, Simon and Mierswa, Ingo. A Hybrid Approach to Feature Selection and Generation Using an Evolutionary Algorithm. No. CI-127/02, Collaborative Research Center 531, University of Dortmund, Dortmund, Germany, 2002.


Ritthoff/etal/2001a Ritthoff, Oliver and Klinkenberg, Ralf and Fischer, Simon and Mierswa, Ingo and Felske, Sven. \sc Yale: Yet Another Machine Learning Environment. In Klinkenberg, Ralf and Ruping, Stefan and Fick, Andreas and Henze, Nicola and Herzog, Christian and Molitor, Ralf and Schroder, Olaf (editors), LLWA 01 -- Tagungsband der GI-Workshop-Woche Lernen -- Lehren -- Wissen -- Adaptivitat, No. 763, pages 84--92, Dortmund, Germany, 2001.


Ritthoff/Klinkenberg/2003a Ritthoff, Oliver and Klinkenberg, Ralf. Evolutionary Feature Space Transformation using Type-Restricted Generators. In Cantu-Paz, E. et al. (editors), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2003) - Part II, pages 1606--1607, Springer, 2003.


Mierswa/etal/2003a Mierswa, Ingo and Klinkenberg, Ralf and Fischer, Simon and Ritthoff, Oliver. A Flexible Platform for Knowledge Discovery Experiments: YALE -- Yet Another Learning Environment. In LLWA 03 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitat, 2003.


Mierswa/2004a Mierswa, Ingo. Automatisierte Merkmalsextraktion aus Audiodaten. Fachbereich Informatik, Universit\"at Dortmund, 2004.
mierswa_2004a.pdf [2600 KB]


Mierswa/2003a Mierswa, Ingo. Beatles vs. Bach: Merkmalsextraktion im Phasenraum von Audiodaten. In LLWA 03 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitat, 2003.


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/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.  


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.


Fischer/etal/2002a Fischer, Simon and Klinkenberg, Ralf and Mierswa, Ingo and Ritthoff, Oliver. \sc Yale: Yet Another Learning Environment -- Tutorial. No. CI-136/02, Collaborative Research Center 531, University of Dortmund, Dortmund, Germany, 2002.


Felske/etal/2003a Felske, Sven and Ritthoff, Oliver and Klinkenberg, Ralf. Bestimmung von Isothermenparametern mit Hilfe des maschinellen Lernens. No. CI-149/03, Sonderforschungsbereich 531, Universitat Dortmund, Dortmund, 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.  


Mierswa/2004b Mierswa, Ingo. Automatic Feature Extraction from Large Time Series. In Weihs, C. and Gaul, W. (editors), Classification -- the Ubiquitous Challenge, Proc. of the 28. Annual Conference of the GfKl 2004, pages 600--607, Springer, 2004.


Mierswa/Geisbe/2004a Mierswa, Ingo and Geisbe, Thorsten. Multikriterielle evolutionare Aufstellungsoptimierung von Chemieanlagen unter Beachtung gewichteter Designregeln. Collaborative Research Center 531, University of Dortmund, Dortmund, Germany, 2004.  


Mierswa/2004c Mierswa, Ingo. Automatic Feature Extraction from Large Time Series. In Abecker, A. and Bickel, S. and Brefeld, U. and Drost, I. and Henze, N. and Herden, O. and Minor, M. and Scheffer, T. and Stojanovic, L. and Weibelzahl, S. (editors), Proc. of LWA 2004 - Lernen - Wissensentdeckung - Adaptivitat, 2004.  


Mierswa/Morik/2005a Mierswa, Ingo and Morik, Katharina. Automatic Feature Extraction for Classifying Audio Data. In Machine Learning Journal, Vol. 58, pages 127--149, 2005.


Mierswa/Wurst/2005a Mierswa, Ingo and Wurst, Michael. Efficient Case Based Feature Construction for Heterogeneous Learning Tasks. No. CI-194/05, Collaborative Research Center 531, University of Dortmund, 2005.


Mierswa/Morik/2005b Mierswa, Ingo and Morik, Katharina. Method trees: building blocks for self-organizable representations of value series: how to evolve representations for classifying audio data. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2005, Workshop on Self-Organization In Representations For Evolutionary Algorithms: Building complexity from simplicity, pages 293--300, New York, NY, USA, ACM, 2005.


Mierswa/Wurst/2005b Mierswa, Ingo and Wurst, Michael. Efficient Feature Construction by Meta Learning -- Guiding the Search in Meta Hypothesis Space. In Proc. of the International Conference on Machine Learning, Workshop on Meta Learning, 2005.


Mierswa/Morik/2005c Mierswa, Ingo and Morik, Katharina. Evolutionäre Aufzucht von Methodenbäumen zur Merkmalsextraktion aus Audiodaten. In Informatik Spektrum, Themenheft Musik, Vol. 28, No. 5, pages 381--388, 2005.


Mierswa/Wurst/2005c Mierswa, Ingo and Wurst, Michael. Efficient Case Based Feature Construction for Heterogeneous Learning Tasks. In Alipio Jorge and Luis Torgo and Pavel Brazdil and Rui Camacho and Joao Gama (editors), Proceedings of the European Conference on Machine Learning (ECML 2005), pages 641--648, Berlin, Springer, 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.


Wurst/etal/2005a Wurst, Michael and Mierswa, Ingo and Morik, Katharina. Structuring Music Collections by Exploiting Peers' Processing. No. 43/05, Collaborative Research Center 475, University of Dortmund, 2005.


Mierswa/Morik/2004a Mierswa, Ingo and Morik, Katharina. Learning Feature Extraction for Learning from Audio Data. No. 55/04, Collaborative Research Center 475, University of Dortmund, 2004.  


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.


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/2006a Mierswa, Ingo. Evolutionary Learning with Kernels: A Generic Solution for Large Margin Problems. In Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2006), 2006.


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 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pages 935--940, ACM, New York, USA, ACM Press, 2006.  


Moerchen/etal/2006a Morchen, Fabian and Mierswa, Ingo and Ultsch, Alfred. Understandable models of music collections based on exhaustive feature generation with temporal statistics. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.


Mierswa/Wurst/2006b Mierswa, Ingo and Wurst, Michael. Sound Multi-Objective Feature Space Transformation for Clustering. In Proceedings of the Knowledge Discovery, Data Mining, and Machine Learning (KDML), pages 330--337, 2006.  


Mierswa/2006b Mierswa, Ingo. Making Indefinite Kernel Learning Practical. Collaborative Research Center 475, University of Dortmund, 2006.


Mierswa/2007c Mierswa, Ingo. Regularization through Multi-Objective Optimization. In Klinkenberg, Ralf and Mierswa, Ingo and Hinneburg, Alexander and Posch, Stefan and Neumann, Steffen (editors), Proc. of LWA 2007 - Lernen - Wissensentdeckung - Adaptivität, 2007.  


Mierswa/Wurst/2006a Mierswa, Ingo and Wurst, Michael. Information Preserving Multi-Objective Feature Selection for Unsupervised Learning. In Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens (editors), GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1545--1552, New York, NY, USA, ACM Press, 2006.