TY - GEN AB - 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. AU - Tornede, Alexander AU - Gehring, Lukas AU - Tornede, Tanja AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke ID - 30865 T2 - Machine Learning TI - Algorithm Selection on a Meta Level ER - TY - GEN AU - Hammer, Barbara AU - Hüllermeier, Eyke AU - Lohweg, Volker AU - Schneider, Alexander AU - Schenck, Wolfram AU - Kuhl, Ulrike AU - Braun, Marco AU - Pfeifer, Anton AU - Holst, Christoph-Alexander AU - Schmidt, Malte AU - Schomaker, Gunnar AU - Tornede, Tanja ID - 36227 TI - Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens ER - TY - CONF AU - Tornede, Tanja AU - Tornede, Alexander AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke ID - 21570 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance ER - TY - GEN AB - Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. While AutoML offers many prospects, it is also known to be quite resource-intensive, which is one of its major points of criticism. The primary cause for a high resource consumption is that many approaches rely on the (costly) evaluation of many machine learning pipelines while searching for good candidates. This problem is amplified in the context of research on AutoML methods, due to large scale experiments conducted with many datasets and approaches, each of them being run with several repetitions to rule out random effects. In the spirit of recent work on Green AI, this paper is written in an attempt to raise the awareness of AutoML researchers for the problem and to elaborate on possible remedies. To this end, we identify four categories of actions the community may take towards more sustainable research on AutoML, i.e. Green AutoML: design of AutoML systems, benchmarking, transparency and research incentives. AU - Tornede, Tanja AU - Tornede, Alexander AU - Hanselle, Jonas Manuel AU - Wever, Marcel Dominik AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 30866 T2 - arXiv:2111.05850 TI - Towards Green Automated Machine Learning: Status Quo and Future Directions ER - TY - CONF AU - Tornede, Tanja AU - Tornede, Alexander AU - Wever, Marcel Dominik AU - Mohr, Felix AU - Hüllermeier, Eyke ID - 17424 T2 - Proceedings of the ECMLPKDD 2020 TI - AutoML for Predictive Maintenance: One Tool to RUL Them All ER - TY - JOUR AB - The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale. AU - Hoffmann, Martin W. AU - Wildermuth, Stephan AU - Gitzel, Ralf AU - Boyaci, Aydin AU - Gebhardt, Jörg AU - Kaul, Holger AU - Amihai, Ido AU - Forg, Bodo AU - Suriyah, Michael AU - Leibfried, Thomas AU - Stich, Volker AU - Hicking, Jan AU - Bremer, Martin AU - Kaminski, Lars AU - Beverungen, Daniel AU - zur Heiden, Philipp AU - Tornede, Tanja ID - 17426 JF - Sensors SN - 1424-8220 TI - Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions ER -