Tuned Data Mining in R
Die Suche nach den besten Parametern für Data Mining Modelle bezeichnet man als "Parameter Tuning" oder kurz "Tuning". Das R-Package TDMR unterstützt den Anwender bei der Suche nach diesen Parametern.
TDMR wurde im Rahmen des Projektes SOMA von Wolfgang Konen und Patrick Koch entwickelt.
Ein Tutorial zu TDMR (in Englisch) führt in die Benutzung ein.
Die TDMR-Dokumentation (in Englisch) erklärt mehr Details zu TDMR.
Mehr Informationen auf der CIOP-Webseite TDMR (in Englisch).
Publikationen TDMR
| 1. | : General Board Game Playing for Education and Research in Generic AI Game Learning. In: Perez, Diego; Mostaghim, Sanaz; Lucas, Simon (Hrsg.): IEEE Conference on Games, London, 2019. |
| 2. | : SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning. arXiv preprint arXiv:1904.08397 2019. |
| 3. | : Solving Optimization Problems with High Conditioning by Means of Online Whitening. In: Lopez-Ibanez, Manuel (Hrsg.): Genetic and Evolutionary Computation Conference 2019 (GECCO'19), Prague, S. 243-244, ACM, 2019. |
| 4. | : How to Solve the Dilemma of Margin-Based Equality Constraint Handling Methods. In: at-Automatisierungstechnik, submitted , 2019. |
| 5. | : Deep Learning mit Keras und Tensorflow. Vortrag auf DEBRL2019 (Digital Exchange Bergisches Rheinland 2019), 2019. |
| 6. | : The GBG Class Interface Tutorial V2.0: General Board Game Playing and Learning. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2019. |
| 7. | : Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen. TH Köln -- University of Applied Sciences, 2019, (Master thesis). |
| 8. | : How to Solve the Dilemma of Margin-Based Equality Handling Methods. In: Hoffmann, Frank; Hüllermeier, Eyke; Mikut, Ralf (Hrsg.): Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018, S. 257-270, KIT Scientific Publishing, Karlsruhe, 2018, ISBN: 978-3-7315-0845-8, (**Young Author Award**). |
| 9. | : Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) TH Köln - University of Applied Science, 2018, (submitted to Archives of Data Sciences, Series A (ECDA'2018), preprint available at http://www.gm.fh-koeln.de/ciopwebpub/Thill18a.d/AoDS2018.pdf). |
| 10. | : The TDMR 2.0 Package: Tuned Data Mining in R. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2018), 2018, (Last update: April 2018 (original version: 2012)). |
| 11. | : The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (03/2018), 2018, (Last update: April 2018 (original version: 2012)). |
| 12. | : Comparing Kriging and Radial Basis Function Surrogates. In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 243-259, Universitätsverlag Karlsruhe, 2017. |
| 13. | : Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation. In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017. |
| 14. | : KI-Agenten fur das Spiel 2048: Untersuchung von Lernalgorithmen für nichtdeterministische Spiele. TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis). |
| 15. | : Selbstlernende Agenten für das skalierbare Spiel Hex: Untersuchung verschiedener KI-Verfahren im GBG-Framework. TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis). |
| 16. | : Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation. In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017. |
| 17. | : Constraint Handling in Efficient Global Optimization. In: Bosman, Peter A N (Hrsg.): Genetic and Evolutionary Computation Conference 2017 (GECCO'17), Berlin, S. 1, ACM, 2017. |
| 18. | : The GBG Class Interface Tutorial: General Board Game Playing and Learning. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, 2017, (e-print published at http://www.gm.fh-koeln.de/ciopwebpub/Kone17a.d/TR-GBG.pdf). |
| 19. | : Identifizierung von Anomalien in Zeitreihen mit Deep Autoencodern. TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis). |
| 20. | : Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets. In: Applied Soft Computing, 61 , S. 377-393, 2017, ISSN: 1568-4946. |
2018 |
The TDMR 2.0 Package: Tuned Data Mining in R Forschungsbericht Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2018), 2018, (Last update: April 2018 (original version: 2012)). |
The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R Forschungsbericht Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (03/2018), 2018, (Last update: April 2018 (original version: 2012)). |
2015 |
SVM ensembles are better when different kernel types are combined Buchkapitel mit eigenem Titel Lausen, Berthold ; Krolak-Schwerdt, Sabine (Hrsg.): Data Science, Learning by Latent Structures, and Knowledge Discovery, S. 191–201, Springer, 2015. |
Efficient multi-criteria optimization on noisy machine learning problems Artikel Applied Soft Computing, 29 , S. 357-370, 2015. |
2013 |
Subsampling strategies in SVM ensembles Konferenzbeitrag Hoffmann, Frank ; Hüllermeier, Eyke (Hrsg.): Proceedings 23. Workshop Computational Intelligence, S. 119–134, Universitätsverlag Karlsruhe, 2013. |
SVM ensembles are better when different kernel types are combined Konferenzbeitrag Lausen, Berthold (Hrsg.): European Conference on Data Analysis (ECDA13), GfKl, 2013. |
2012 |
The TDMR Package: Tuned Data Mining in R Forschungsbericht Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2012), 2012, (Last update: June 2017). |
The TDMR Tutorial: Examples for Tuned Data Mining in R Forschungsbericht Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (03/2012), 2012, (Last update: May, 2016). |
Efficient sampling and handling of variance in tuning data mining models Konferenzbeitrag Coello Coello, Carlos ; Cutello, Vincenzo ; others, (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 195–205, Springer, Heidelberg, 2012. |
Tuning and Evolution of Support Vector Kernels Artikel Evolutionary Intelligence, 5 , S. 153–170, 2012. |
2011 |
Self-configuration from a Machine-Learning Perspective Forschungsbericht Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (05/11; arXiv: 1105.1951), 2011, ISSN: 2191-365X, (e-print published at http://arxiv.org/abs/1105.1951 and Dagstuhl Preprint Archive, Workshop 11181 "Organic Computing -- Design of Self-Organizing Systems"). |
On the Tuning and Evolution of Support Vector Kernels Forschungsbericht Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science, (04/11), 2011, ISSN: 2191-365X. |
Tuned Data Mining: A Benchmark Study on Different Tuners Forschungsbericht Cologne University of Applied Sciences (03/11), 2011. |
Ensemble Based Optimization and Tuning Algorithms Konferenzbeitrag Hoffmann, Frank ; Hüllermeier, Eyke (Hrsg.): Proceedings 21. Workshop Computational Intelligence, S. 119–134, Universitätsverlag Karlsruhe, 2011. |
Tuned Data Mining in R Konferenzbeitrag Hoffmann, Frank ; Hüllermeier, Eyke (Hrsg.): Proceedings 21. Workshop Computational Intelligence, S. 147–160, Universitätsverlag Karlsruhe, 2011. |
Tuned Data Mining: A Benchmark Study on Different Tuners Konferenzbeitrag Krasnogor, Natalio (Hrsg.): GECCO '11: Proceedings of the 13th Annual Conference on Genetic andEvolutionary Computation, S. 1995–2002, 2011. |
2010 |
Optimizing Support Vector Machines for Stormwater Prediction Konferenzbeitrag Bartz-Beielstein, Thomas ; Chiarandini, M; Paquete, L; Preuss, Mike (Hrsg.): Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010, S. 47–59, TU Dortmund, 2010. |
Optimization of Support Vector Regression Models for Stormwater Prediction Konferenzbeitrag Hoffmann, Frank ; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, S. 146–160, Universitätsverlag Karlsruhe, 2010. |
Parameter-Tuned Data Mining: A General Framework Konferenzbeitrag Hoffmann, Frank ; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, Universitätsverlag Karlsruhe, 2010. |
2009 |
at-Automatisierungstechnik, 57 (3), S. 155–166, 2009. |
