Monthly Archives: March 2017

New Article: Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data #ComputationalIntelligence #MachineLearning

The article “Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data” (Alexis Sardá-Espinosaa, Subanatarajan Subbiah, Thomas Bartz-Beielstein), which will be published in the journal “Engineering Applications of Artificial Intelligence“, can be freely downloaded until May 20, 2017 from
Anyone who clicks on the link until May 20, 2017, will be taken to the final version of your article on ScienceDirect for free. No sign up or registration is needed – just click and read!

Abstract: Computational tools for the analysis of data gathered by monitoring systems are necessary because the amount of data steadily increases. Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner.

Keywords: Decision tree; Conditional inference tree; Health condition monitoring; Machine learning; Knowledge extraction

Authors: Alexis Sardá-Espinosa (ABB AG German Research Center, Technische Hochschule Köln),  Subanatarajan Subbiah (ABB AG German Research Center), Thomas Bartz-Beielstein (Technische Hochschule Köln)

Here is the DOI: 10.1016/j.engappai.2017.03.008



Publish or …

A successful week in the SPOTSeven lab. Three papers (1 x journal, 2 x conference) were accepted for publication:

  • A. Sarda-Espinosa, S. Subbiah, and T. Bartz-Beielstein. Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data. Engineering Applications of Artificial Intelligence, 2017.
  • M. Zaefferer, A. Fischbach, B. Naujoks, and T. Bartz-Beielstein. Simulation-based test functions for optimization algorithms. In GECCO ’17: Proceedings of the 2017 Annual Conference on Genetic and Evolutionary Computation, 2017.
  • J. Heinerman, J. Stork, M. A. R. Coy, J. Hubert, T. Bartz-Beielstein, A. Eiben, and E. Haasdijk. Is social learning more than parameter tuning? In GECCO ’17: Proceedings of the 2017 Annual Conference on Genetic and Evolutionary Computation, 2017.

#worldwaterday: WDR zeigt Kurz-Reportage über Trinkwasserprojekt der TH Köln

v.l.: Kamerateam des WDR; Prof. Bongards, Prof. Bartz-Beielstein, Steffen Moritz, Dr. Peter Kern (sitzend)

v.l.: Kamerateam des WDR; Manuela Klein (Reporterin des WDR) mit Dr. Peter Kern im Interview

Am heutigen Tag des Wassers zeigt das Fernsehprogramm des WDR in der Sendung „Lokalzeit“ eine Reportage über das Forschungsprojekt „IMProvT“ der Technischen Hochschule Köln. Hierzu befragte ein Team des WDR die beteiligten Wissenschaftler in der Forschungsanlage auf :metabolon, dem Forschungs- und Kompetenzstandort der TH Köln in Lindlar. Continue reading

#worldwaterday WDR berichtet über Forschungsprojekt IMProVt @TH_Koeln

Heute Abend bringt der Westdeutsche Rundfunk in der Sendung “Lokalzeit Köln” um 19:30 Uhr einen Kurzbeitrag über das von den Professoren Bongards und Bartz-Beielstein an der TH Köln durchgeführte Forschungsprojekt “IMProvT — Intelligente Messverfahren zur Prozessoptimierung von Trinkwasserbereitstellung und Verteilung”.
Das Projekt IMProvT behandelt die Gewinnung und Nutzung mehrdimensionaler Prozessdaten zur energie- und ressourceneffizienten Optimierung und Prozesssteuerung bei der Trinkwasseraufbereitung. Zentraler Ansatzpunkt ist die Erzeugung kunden- und betriebsoptimierter Informationen sowie die Anpassung der einzelnen Trinkwasserprozessschritte bzw. des Netzmanagements an die aktuelle Situation auf Basis von Computational Intelligence (CI)-Methoden. Continue reading

Interested in #ModelBased Methods for #Optimization? #SPOT2

If you are interested in “Model-based Methods for Continuous and Discrete Global Optimization“, you can freely access the article until April 11, 2017:
The SPO Toolbox was used for performing the experiments described in this article. The Sequential Parameter Optimization Toolbox 2.0.1 is a major update of the SPOT R package. It provides a set of tools for model based optimization and tuning of algorithms. It includes surrogate models, optimizers and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources. See: