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
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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
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.
The submission of Extended Abstracts to the 12th edition of the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems, EUROGEN-2017, is now open! Continue reading
In June this year, ACM will celebrate 50 Years of the A.M. Turing Award, which recognizes major contributions of lasting importance in computing. Through the years, it has become the most prestigious award in the field, often referred to as the “Nobel Prize of computing.” Continue reading
Das Pionier-Unternehmen der Heimarbeit, IBM, schafft Stück für Stück das Home Office ab. Mehr: http://www.faz.net/-gyl-8w6x1
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