Tanja Tornede, M.Sc.

Publications

• Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier (2022)
  Algorithm Selection on a Meta Level.
  In: arXiv:2107.09414 Machine Learning.
  
  
  The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.
• Hammer, Barbara, Eyke Hüllermeier, Volker Lohweg, Alexander Schneider, Wolfram Schenck, Ulrike Kuhl, Marco Braun, et al. (2022)
  Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen.
  In: Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens.
• Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier (2021)
  Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.
  In: Proceedings of the Genetic and Evolutionary Computation Conference.
• Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier (2021)
  Towards Green Automated Machine Learning: Status Quo and Future Directions.
  In:ArXiv:2111.05850.
• Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier (2020)
  AutoML for Predictive Maintenance: One Tool to RUL Them All.
  In: Proceedings of the ECMLPKDD 2020.
• Hoffmann, Martin W., Stephan Wildermuth, Ralf Gitzel, Aydin Boyaci, Jörg Gebhardt, Holger Kaul, Ido Amihai, et al. (2020)
  Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions.
  In: Sensors, 2020.