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
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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
Weitere Informationen: www.ml4cps.com/call_for_papers
The article “Model-based Methods for Continuous and Discrete Global Optimization” by T. Bartz-Beielstein and M. Zaefferer is available online: http://www.sciencedirect.com/science/article/pii/S1568494617300546
A preprint can be downloaded from “Cologne Open Science”: urn:nbn:de:hbz:832-cos4-4356
The use of surrogate models is a standard method for dealing with complex real-world optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article takes this development into consideration. The first part presents a survey of model-based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools. The second part examines discrete optimization problems. Here, six strategies for dealing with discrete data structures are introduced. A new approach for combining surrogate information via stacking is proposed in the third part. The implementation of this approach will be available in the open source R package SPOT2. The article concludes with a discussion of recent developments and challenges in continuous and discrete application domains.
- “Bevor Sie “blind” Daten sammeln und “alles” vernetzen (oder vernetzbar machen), holen Sie sich Rat über die mathematischen Möglichkeiten, was mit welchen Daten erreichbar ist.”
- “Kaufen Sie keine Software, auf der nicht das Label “Mathematik inside” steht. Geben Sie sich nicht mit weniger als mathematischer Optimierung zufrieden. Ihre Mitbewerber werden es auch nicht tun.”
Den gesamten Beitrag finden Sie unter: https://mluebbecke.wordpress.com/2015/12/16/industrie-5-0/
Industry is faced with solving complex optimization problems on a day to day basis in different domains including transportation, data mining, computer vision, computer security, robotics and scheduling amongst others. #machinelearning and search algorithms play an important role in solving such problems.