Systematical Optimization of Models in Automation and IT
This BMBF-sponsored project was conducted in 2009-2013. Project partner were Leiden University, NL, University Bochum, Nurogames GmbH, Cologne and divis GmbH, Dortmund.
Systematic optimization of models for complex applications in IT and automation, here with the goal of forecasting and optimal control of plants or processes, is the topic of this project. It poses still a great challenge for the practioner in computer science or engineering. In many cases it is not alone a problem of the right model parametrization, but also a task of intelligent data preprocessing and data selection. Here the project SOMA aims at developing and offering new solutions and tools.
Under the umbrella of SOMA several sub-projects were undertaken: Tuned data mining (TDMR), gesture recognition, reinforcement learning for strategic games, intelligent methods for feature generation like slow feature analysis (SFA) or n-tuple systems.
Topics: Applied computer science, modeling, simulation, learning system, computational intelligence (evolutionary algorithms, neural networks), data mining.
Publications SOMA
2018
Konen, Wolfgang; Koch, Patrick
The TDMR 2.0 Package: Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 02/2018, 2018, (Last update: April 2018 (original version: 2012)).
@techreport{Kone18a,
title = {The TDMR 2.0 Package: Tuned Data Mining in R},
author = {Wolfgang Konen and Patrick Koch},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone12a.d/Kone12a.pdf},
year = {2018},
date = {2018-04-01},
number = {02/2018},
address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
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Konen, Wolfgang; Koch, Patrick
The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 03/2018, 2018, (Last update: April 2018 (original version: 2012)).
@techreport{Kone18b,
title = {The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R},
author = {Wolfgang Konen and Patrick Koch},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Kone12b.d/Kone12b.pdf},
year = {2018},
date = {2018-04-01},
number = {03/2018},
address = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
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2015
Stork, Jörg; Ramos, Ricardo; Koch, Patrick; Konen, Wolfgang
SVM ensembles are better when different kernel types are combined Book Section
In: Lausen, Berthold; Krolak-Schwerdt, Sabine (Ed.): Data Science, Learning by Latent Structures, and Knowledge Discovery, pp. 191–201, Springer, 2015.
@incollection{stork15,
title = {SVM ensembles are better when different kernel types are combined},
author = { Jörg Stork and Ricardo Ramos and Patrick Koch and Wolfgang Konen},
editor = {Lausen, Berthold and Krolak-Schwerdt, Sabine},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/storkECDA-2013.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Data Science, Learning by Latent Structures, and Knowledge Discovery},
pages = {191--201},
publisher = {Springer},
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2013
Koch, Patrick; Konen, Wolfgang
Subsampling strategies in SVM ensembles Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Ed.): Proceedings 23. Workshop Computational Intelligence, pp. 119–134, Universitätsverlag Karlsruhe, 2013.
@inproceedings{Koch13a,
title = {Subsampling strategies in SVM ensembles},
author = { Patrick Koch and Wolfgang Konen},
editor = {Hoffmann, Frank and Hüllermeier, Eyke},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/kochGMA2013.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings 23. Workshop Computational Intelligence},
pages = {119--134},
publisher = {Universitätsverlag Karlsruhe},
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Stork, Jörg; Ramos, Ricardo; Koch, Patrick; Konen, Wolfgang
SVM ensembles are better when different kernel types are combined Proceedings Article
In: Lausen, Berthold (Ed.): European Conference on Data Analysis (ECDA13), GfKl, 2013.
@inproceedings{stork13a,
title = {SVM ensembles are better when different kernel types are combined},
author = { Jörg Stork and Ricardo Ramos and Patrick Koch and Wolfgang Konen},
editor = {Lausen, Berthold},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/storkECDA-2013.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {European Conference on Data Analysis (ECDA13)},
publisher = {GfKl},
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pubstate = {published},
tppubtype = {inproceedings}
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2012
Konen, Wolfgang; Koch, Patrick
The TDMR Package: Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 02/2012, 2012, (Last update: June 2017).
@techreport{Kone12ab,
title = {The TDMR Package: Tuned Data Mining in R},
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year = {2012},
date = {2012-11-01},
number = {02/2012},
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Konen, Wolfgang; Koch, Patrick
The TDMR Tutorial: Examples for Tuned Data Mining in R Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 03/2012, 2012, (Last update: May, 2016).
@techreport{Kone12bb,
title = {The TDMR Tutorial: Examples for Tuned Data Mining in R},
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year = {2012},
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number = {03/2012},
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Koch, Patrick; Konen, Wolfgang
Efficient sampling and handling of variance in tuning data mining models Proceedings Article
In: Coello Coello, Carlos; Cutello, Vincenzo; others, (Ed.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, pp. 195–205, Springer, Heidelberg, 2012.
@inproceedings{Koch12a,
title = {Efficient sampling and handling of variance in tuning data mining models},
author = { Patrick Koch and Wolfgang Konen},
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Thill, Markus; Koch, Patrick; Konen, Wolfgang
Reinforcement learning with n-tuples on the game Connect-4 Proceedings Article
In: Coello Coello, Carlos; Cutello, Vincenzo; others, (Ed.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, pp. 184–194, Springer, Heidelberg, 2012.
@inproceedings{Thil12,
title = {Reinforcement learning with n-tuples on the game Connect-4},
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year = {2012},
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Koch, Patrick; Bischl, Bernd; Flasch, Oliver; Bartz-Beielstein, Thomas; Weihs, Claus; Konen, Wolfgang
Tuning and Evolution of Support Vector Kernels Journal Article
In: Evolutionary Intelligence, vol. 5, pp. 153–170, 2012.
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title = {Tuning and Evolution of Support Vector Kernels},
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year = {2012},
date = {2012-01-01},
journal = {Evolutionary Intelligence},
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pages = {153--170},
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2011
Konen, Wolfgang
Der SFA-Algorithmus für Klassifikation Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 08/11, 2011, ISSN: 2191-365X.
@techreport{Kone11e,
title = {Der SFA-Algorithmus für Klassifikation},
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url = {http://www.gm.fh-koeln.de/ciopwebpub/Konen11e.d/Konen11e.pdf},
issn = {2191-365X},
year = {2011},
date = {2011-11-01},
number = {08/11},
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Konen, Wolfgang
SFA classification with few training data: Improvements with parametric bootstrap Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, no. 09/11, 2011, ISSN: 2191-365X.
@techreport{Kone11f,
title = {SFA classification with few training data: Improvements with parametric bootstrap},
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Koch, Patrick; Bischl, Bernd; Flasch, Oliver; Bartz-Beielstein, Thomas; Konen, Wolfgang
On the Tuning and Evolution of Support Vector Kernels Technical Report
Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science, no. 04/11, 2011, ISSN: 2191-365X.
@techreport{Koch11a,
title = {On the Tuning and Evolution of Support Vector Kernels},
author = { Patrick Koch and Bernd Bischl and Oliver Flasch and Thomas Bartz-Beielstein and Wolfgang Konen},
url = {http://www.gm.fh-koeln.de/ciopwebpub/Koch11a.d/svmTuning.pdf},
issn = {2191-365X},
year = {2011},
date = {2011-03-01},
number = {04/11},
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Konen, Wolfgang; Koch, Patrick; Flasch, Oliver; Bartz-Beielstein, Thomas; Friese, Martina; Naujoks, Boris
Tuned Data Mining: A Benchmark Study on Different Tuners Technical Report
Cologne University of Applied Sciences no. 03/11, 2011.
@techreport{Kone11b,
title = {Tuned Data Mining: A Benchmark Study on Different Tuners},
author = { Wolfgang Konen and Patrick Koch and Oliver Flasch and Thomas Bartz-Beielstein and Martina Friese and Boris Naujoks},
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Koch, Patrick; Konen, Wolfgang; Naujoks, Boris; Flasch, Oliver; Friese, Martina; Zaefferer, Martin; Bartz-Beielstein, Thomas
Tuned Data Mining in R Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Ed.): Proceedings 21. Workshop Computational Intelligence, pp. 147–160, Universitätsverlag Karlsruhe, 2011.
@inproceedings{Koch11b,
title = {Tuned Data Mining in R},
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url = {http://www.gm.fh-koeln.de/~konen/Publikationen/Koch11b.pdf},
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Konen, Wolfgang; Koch, Patrick; Flasch, Oliver; Bartz-Beielstein, Thomas; Friese, Martina; Naujoks, Boris
Tuned Data Mining: A Benchmark Study on Different Tuners Proceedings Article
In: Krasnogor, Natalio (Ed.): GECCO '11: Proceedings of the 13th Annual Conference on Genetic andEvolutionary Computation, pp. 1995–2002, 2011.
@inproceedings{Kone11d,
title = {Tuned Data Mining: A Benchmark Study on Different Tuners},
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Konen, Wolfgang; Koch, Patrick
The slowness principle: SFA can detect different slow components in nonstationary time series Journal Article
In: International Journal of Innovative Computing and Applications (IJICA), vol. 3, no. 1, pp. 3–10, 2011.
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title = {The slowness principle: SFA can detect different slow components in nonstationary time series},
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url = {http://www.gm.fh-koeln.de/~konen/Publikationen/IJICA2010-howslow.pdf},
doi = {10.1504/IJICA.2011.037946},
year = {2011},
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journal = {International Journal of Innovative Computing and Applications (IJICA)},
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2010
Koch, Patrick; Konen, Wolfgang; Hein, Kristine
Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap Proceedings Article
In: 2010 International Joint Conference on Neural Networks, 2010.
@inproceedings{Koch10a,
title = {Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap},
author = { Patrick Koch and Wolfgang Konen and Kristine Hein},
url = {http://www.gm.fh-koeln.de/~konen/Publikationen/WCCI-10_wii-gesture.pdf},
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date = {2010-01-01},
booktitle = {2010 International Joint Conference on Neural Networks},
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Koch, Patrick; Konen, Wolfgang; Flasch, Oliver; Bartz-Beielstein, Thomas
Optimizing Support Vector Machines for Stormwater Prediction Proceedings Article
In: Bartz-Beielstein, Thomas; Chiarandini, M.; Paquete, L.; Preuss, Mike (Ed.): Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010, pp. 47–59, TU Dortmund, 2010.
@inproceedings{Koch10b,
title = {Optimizing Support Vector Machines for Stormwater Prediction},
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editor = {Bartz-Beielstein, Thomas and Chiarandini, M. and Paquete, L. and Preuss, Mike},
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Koch, Patrick; Konen, Wolfgang; Flasch, Oliver; Bartz-Beielstein, Thomas
Optimization of Support Vector Regression Models for Stormwater Prediction Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Ed.): Proceedings 20. Workshop Computational Intelligence, pp. 146–160, Universitätsverlag Karlsruhe, 2010.
@inproceedings{Koch10c,
title = {Optimization of Support Vector Regression Models for Stormwater Prediction},
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Konen, Wolfgang; Koch, Patrick; Flasch, Oliver; Bartz-Beielstein, Thomas
Parameter-Tuned Data Mining: A General Framework Proceedings Article
In: Hoffmann, Frank; Hüllermeier, Eyke (Ed.): Proceedings 20. Workshop Computational Intelligence, Universitätsverlag Karlsruhe, 2010.
@inproceedings{Kone10a,
title = {Parameter-Tuned Data Mining: A General Framework},
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Konen, Wolfgang; Koch, Patrick
How slow is slow? SFA detects signals that are slower than the driving force Proceedings Article
In: Filipic, Bogdan; Silc, Juri (Ed.): Proc. 4th Int. Conf. on Bioinspired Optimization Methods and their Applications, BIOMA, Ljubljana, Slovenia, 2010.
@inproceedings{KonK10a,
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Hein, Kristine
Gestenerkennung mit Slow Feature Analysis (SFA) - Klassifizierung von beschleunigungsbasierten 3D-Gesten des Wii-Controllers Masters Thesis
TH Köln -- University of Applied Sciences, 2010, (Master thesis, 3rd prize in Opitz award 2011).
@mastersthesis{Hein10,
title = {Gestenerkennung mit Slow Feature Analysis (SFA) - Klassifizierung von beschleunigungsbasierten 3D-Gesten des Wii-Controllers},
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2009
Konen, Wolfgang
How slow is slow? SFA detects signals that are slower than the driving force Technical Report
Cologne University of Applied Sciences no. 05/09, 2009, (e-print published at http://arxiv.org/abs/0911.4397).
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Konen, Wolfgang
On the numeric stability of the SFA implementation sfa-tk Technical Report
Cologne University of Applied Sciences no. 05/10, 2009, (e-print published at http://arxiv.org/abs/0912.1064).
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