Here is the link to this year’s useR! talk about Sequential Parameter Optimization presented by Sebastian Krey:
The Revised Sequential Parameter Optimization Toolbox
Last week the useR! 2017 conference took place in Brussels, Belgium. The annual useR! conference is the main meeting of the international R user and developer community. Over 1000 participants came to listen to a broad spectrum of talks ranging from technical and R-related computing issues to general statistical topics of current interest .
Here you find some impressions from the conference: Pictures
The SPOTSeven team gave two presentations:
Here you can find the slides:
The talks enjoyed great participation and resulted in lots of interesting discussions afterwards.
The publication “How to deal with Missing Data in Time Series and the imputeTS package” (Steffen Moritz, Thomas Bartz-Beielstein)
has been accepted for presentation as a useR! Talk.
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: https://CRAN.R-project.org/package=SPOT
The article imputeTS: Time Series Missing Value Imputation in R by Steffen Moritz and Thomas Bartz-Beielstein has been accepted for publication in the R Journal. Continue reading
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
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.