[{"place":"Paderborn","title":"Configuration and Evaluation","department":[{"_id":"7"}],"project":[{"grant_number":"160364472","name":"SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ","_id":"1"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: Konfiguration und Bewertung (B02)","grant_number":"160364472","_id":"10"}],"editor":[{"full_name":"Haake, Claus-Jochen","first_name":"Claus-Jochen","last_name":"Haake"},{"last_name":"Meyer auf der Heide","full_name":"Meyer auf der Heide, Friedhelm","first_name":"Friedhelm"},{"first_name":"Marco","full_name":"Platzner, Marco","last_name":"Platzner"},{"full_name":"Wachsmuth, Henning","first_name":"Henning","last_name":"Wachsmuth"},{"full_name":"Wehrheim, Heike","first_name":"Heike","last_name":"Wehrheim"}],"date_updated":"2023-07-07T11:20:12Z","oa":"1","doi":"10.5281/zenodo.8068466","series_title":"Verlagsschriftenreihe des Heinz Nixdorf Instituts","language":[{"iso":"eng"}],"user_id":"477","ddc":["040"],"file":[{"creator":"florida","file_id":"45885","file_size":895091,"relation":"main_file","date_updated":"2023-07-07T11:20:11Z","content_type":"application/pdf","file_name":"B2-Chapter-SFB-Buch-Final.pdf","date_created":"2023-07-07T07:50:34Z","access_level":"open_access"}],"file_date_updated":"2023-07-07T11:20:11Z","publication":"On-The-Fly Computing -- Individualized IT-services in dynamic markets","publisher":"Heinz Nixdorf Institut, Universität Paderborn","author":[{"id":"43980","last_name":"Hanselle","orcid":"0000-0002-1231-4985","full_name":"Hanselle, Jonas Manuel","first_name":"Jonas Manuel"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"first_name":"Axel-Cyrille","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","id":"65716"},{"full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","first_name":"Mohamed","id":"67234","last_name":"Sherif"},{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"}],"date_created":"2023-07-07T07:50:53Z","has_accepted_license":"1","status":"public","volume":412,"_id":"45884","intvolume":" 412","page":"85-104","citation":{"bibtex":"@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023, place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Configuration and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466}, booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets}, publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts} }","mla":"Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104, doi:10.5281/zenodo.8068466.","chicago":"Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration and Evaluation.” In On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.5281/zenodo.8068466.","apa":"Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466","ama":"Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104. doi:10.5281/zenodo.8068466","ieee":"J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.","short":"J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A. Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 85–104."},"year":"2023","type":"book_chapter"},{"date_updated":"2022-04-12T12:01:15Z","_id":"30868","type":"preprint","citation":{"ieee":"E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,” arXiv:2202.01651. 2022.","short":"E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K. Tierney, ArXiv:2202.01651 (2022).","bibtex":"@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651}, author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022} }","mla":"Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.” ArXiv:2202.01651, 2022.","chicago":"Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated Algorithm Configuration.” ArXiv:2202.01651, 2022.","ama":"Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm Configuration. arXiv:220201651. Published online 2022.","apa":"Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. In arXiv:2202.01651."},"year":"2022","language":[{"iso":"eng"}],"title":"A Survey of Methods for Automated Algorithm Configuration","user_id":"38209","external_id":{"arxiv":["2202.01651"]},"abstract":[{"lang":"eng","text":"Algorithm configuration (AC) is concerned with the automated search of the\r\nmost suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently a wide variety of AC problem variants and methods proposed in the\r\nliterature. Existing reviews do not take into account all derivatives of the AC\r\nproblem, nor do they offer a complete classification scheme. To this end, we\r\nintroduce taxonomies to describe the AC problem and features of configuration\r\nmethods, respectively. We review existing AC literature within the lens of our\r\ntaxonomies, outline relevant design choices of configuration approaches,\r\ncontrast methods and problem variants against each other, and describe the\r\nstate of AC in industry. Finally, our review provides researchers and\r\npractitioners with a look at future research directions in the field of AC."}],"status":"public","project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"date_created":"2022-04-12T12:00:08Z","author":[{"full_name":"Schede, Elias","first_name":"Elias","last_name":"Schede"},{"first_name":"Jasmin","full_name":"Brandt, Jasmin","last_name":"Brandt"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"id":"76599","last_name":"Bengs","full_name":"Bengs, Viktor","first_name":"Viktor"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"full_name":"Tierney, Kevin","first_name":"Kevin","last_name":"Tierney"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"publication":"arXiv:2202.01651"},{"language":[{"iso":"eng"}],"type":"preprint","year":"2022","citation":{"chicago":"Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” Machine Learning, 2022.","ama":"Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. Machine Learning. Published online 2022.","apa":"Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In Machine Learning.","bibtex":"@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2022} }","mla":"Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine Learning, 2022.","short":"A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022).","ieee":"A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” Machine Learning. 2022."},"_id":"30865","date_updated":"2022-08-24T12:45:39Z","status":"public","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"date_created":"2022-04-12T11:55:18Z","author":[{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"last_name":"Gehring","full_name":"Gehring, Lukas","first_name":"Lukas"},{"id":"40795","last_name":"Tornede","full_name":"Tornede, Tanja","first_name":"Tanja"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publication":"Machine Learning","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"38209","title":"Algorithm Selection on a Meta Level","abstract":[{"lang":"eng","text":"The problem of selecting an algorithm that appears most suitable for a\r\nspecific instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability problem, is called instance-specific algorithm selection. Over\r\nthe past decade, the problem has received considerable attention, resulting in\r\na number of different methods for algorithm selection. Although most of these\r\nmethods are based on machine learning, surprisingly little work has been done\r\non meta learning, that is, on taking advantage of the complementarity of\r\nexisting algorithm selection methods in order to combine them into a single\r\nsuperior algorithm selector. In this paper, we introduce the problem of meta\r\nalgorithm selection, which essentially asks for the best way to combine a given\r\nset of algorithm selectors. We present a general methodological framework for\r\nmeta algorithm selection as well as several concrete learning methods as\r\ninstantiations of this framework, essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors can significantly outperform single\r\nalgorithm selectors and have the potential to form the new state of the art in\r\nalgorithm selection."}],"external_id":{"arxiv":["2107.09414"]}},{"date_updated":"2022-08-24T12:52:06Z","_id":"33090","doi":"10.1007/s40194-022-01339-9","year":"2022","type":"journal_article","citation":{"ama":"Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. Published online 2022. doi:10.1007/s40194-022-01339-9","apa":"Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. Welding in the World. https://doi.org/10.1007/s40194-022-01339-9","chicago":"Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner, and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, 2022. https://doi.org/10.1007/s40194-022-01339-9.","mla":"Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in the World, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.","bibtex":"@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials}, DOI={10.1007/s40194-022-01339-9}, journal={Welding in the World}, publisher={Springer Science and Business Media LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }","short":"K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022).","ieee":"K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,” Welding in the World, 2022, doi: 10.1007/s40194-022-01339-9."},"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"AbstractHeated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality."}],"title":"A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials","user_id":"38209","keyword":["Metals and Alloys","Mechanical Engineering","Mechanics of Materials"],"publication":"Welding in the World","publisher":"Springer Science and Business Media LLC","author":[{"full_name":"Gevers, Karina","first_name":"Karina","id":"83151","last_name":"Gevers"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"first_name":"Volker","full_name":"Schöppner, Volker","last_name":"Schöppner","id":"20530"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publication_status":"published","publication_identifier":{"issn":["0043-2288","1878-6669"]},"date_created":"2022-08-24T12:51:07Z","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"_id":"3","name":"SFB 901 - B: SFB 901 - Project Area B"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"status":"public"},{"language":[{"iso":"eng"}],"page":"1-1","year":"2021","type":"journal_article","citation":{"short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276","apa":"Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276."},"doi":"10.1109/tpami.2021.3051276","date_updated":"2022-01-06T06:54:42Z","_id":"21004","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2021-01-16T14:48:13Z","status":"public","publication_status":"published","publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","author":[{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","last_name":"Wever","id":"33176"},{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"user_id":"5786","title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation","abstract":[{"lang":"eng","text":"Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers."}]},{"date_updated":"2022-01-06T06:54:45Z","_id":"21092","language":[{"iso":"eng"}],"citation":{"mla":"Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE.","bibtex":"@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke} }","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, n.d.","apa":"Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.","ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.","ieee":"F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence.","short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.)."},"year":"2021","type":"journal_article","user_id":"5786","title":"Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning","abstract":[{"lang":"eng","text":"Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout.\r\nIn this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions."}],"date_created":"2021-01-27T13:45:52Z","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"status":"public","publication_status":"accepted","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"publisher":"IEEE"},{"date_updated":"2022-01-06T06:55:06Z","_id":"21570","conference":{"start_date":"2021-07-10","name":"Genetic and Evolutionary Computation Conference","end_date":"2021-07-14"},"language":[{"iso":"eng"}],"type":"conference","citation":{"mla":"Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","bibtex":"@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021} }","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference, 2021.","ama":"Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings of the Genetic and Evolutionary Computation Conference. ; 2021.","apa":"Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference.","ieee":"T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021.","short":"T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021."},"year":"2021","user_id":"5786","title":"Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance","author":[{"full_name":"Tornede, Tanja","first_name":"Tanja","id":"40795","last_name":"Tornede"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","date_created":"2021-03-26T09:14:19Z","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}]},{"language":[{"iso":"eng"}],"citation":{"ama":"Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021.","apa":"Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual).","chicago":"Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021.","bibtex":"@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021} }","mla":"Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021.","short":"E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.","ieee":"E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021."},"year":"2021","type":"conference","_id":"22913","date_updated":"2022-01-06T06:55:43Z","conference":{"name":"ECML/PKDD Workshop on Automating Data Science","start_date":"2021-09-13","location":"Bilbao (Virtual)","end_date":"2021-09-17"},"status":"public","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"date_created":"2021-08-02T07:46:29Z","author":[{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"}],"quality_controlled":"1","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","title":"Automated Machine Learning, Bounded Rationality, and Rational Metareasoning"},{"_id":"22914","date_updated":"2022-01-06T06:55:43Z","conference":{"end_date":"2021-07-23","name":"8th ICML Workshop on Automated Machine Learning","start_date":"2021-07-23","location":"Virtual"},"type":"conference","citation":{"short":"F. Mohr, M.D. Wever, in: 2021.","ieee":"F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual, 2021.","apa":"Mohr, F., & Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual.","ama":"Mohr F, Wever MD. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. In: ; 2021.","chicago":"Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” 2021.","bibtex":"@inproceedings{Mohr_Wever_2021, title={Replacing the Ex-Def Baseline in AutoML by Naive AutoML}, author={Mohr, Felix and Wever, Marcel Dominik}, year={2021} }","mla":"Mohr, Felix, and Marcel Dominik Wever. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 2021."},"year":"2021","language":[{"iso":"eng"}],"title":"Replacing the Ex-Def Baseline in AutoML by Naive AutoML","user_id":"5786","author":[{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","date_created":"2021-08-02T07:48:07Z"},{"date_updated":"2022-04-12T12:01:23Z","_id":"30866","language":[{"iso":"eng"}],"citation":{"short":"T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier, ArXiv:2111.05850 (2021).","ieee":"T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier, “Towards Green Automated Machine Learning: Status Quo and Future Directions,” arXiv:2111.05850. 2021.","chicago":"Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” ArXiv:2111.05850, 2021.","apa":"Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., & Hüllermeier, E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions. In arXiv:2111.05850.","ama":"Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850. Published online 2021.","mla":"Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo and Future Directions.” ArXiv:2111.05850, 2021.","bibtex":"@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850}, author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }"},"type":"preprint","year":"2021","abstract":[{"lang":"eng","text":"Automated machine learning (AutoML) strives for the automatic configuration\r\nof machine learning algorithms and their composition into an overall (software)\r\nsolution - a machine learning pipeline - tailored to the learning task\r\n(dataset) at hand. Over the last decade, AutoML has developed into an\r\nindependent research field with hundreds of contributions. While AutoML offers\r\nmany prospects, it is also known to be quite resource-intensive, which is one\r\nof its major points of criticism. The primary cause for a high resource\r\nconsumption is that many approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines while searching for good candidates. This problem is\r\namplified in the context of research on AutoML methods, due to large scale\r\nexperiments conducted with many datasets and approaches, each of them being run\r\nwith several repetitions to rule out random effects. In the spirit of recent\r\nwork on Green AI, this paper is written in an attempt to raise the awareness of\r\nAutoML researchers for the problem and to elaborate on possible remedies. To\r\nthis end, we identify four categories of actions the community may take towards\r\nmore sustainable research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking, transparency and research incentives."}],"external_id":{"arxiv":["2111.05850"]},"user_id":"38209","title":"Towards Green Automated Machine Learning: Status Quo and Future Directions","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"publication":"arXiv:2111.05850","author":[{"last_name":"Tornede","id":"40795","first_name":"Tanja","full_name":"Tornede, Tanja"},{"first_name":"Alexander","full_name":"Tornede, Alexander","last_name":"Tornede","id":"38209"},{"full_name":"Hanselle, Jonas Manuel","orcid":"0000-0002-1231-4985","first_name":"Jonas Manuel","id":"43980","last_name":"Hanselle"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"date_created":"2022-04-12T11:57:15Z","status":"public"},{"title":"Automated Machine Learning for Multi-Label Classification","department":[{"_id":"355"}],"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"publication_status":"published","date_updated":"2022-04-13T09:39:56Z","oa":"1","doi":"10.17619/UNIPB/1-1302","language":[{"iso":"eng"}],"user_id":"33176","ddc":["000"],"file":[{"relation":"main_file","date_updated":"2022-04-13T09:39:56Z","content_type":"application/pdf","creator":"wever","file_id":"30886","file_size":8098177,"access_level":"open_access","file_name":"dissertation_publish_upload.pdf","date_created":"2022-04-13T09:35:25Z"}],"file_date_updated":"2022-04-13T09:39:56Z","author":[{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"}],"date_created":"2021-11-08T14:05:19Z","status":"public","has_accepted_license":"1","_id":"27284","supervisor":[{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"citation":{"bibtex":"@book{Wever_2021, title={Automated Machine Learning for Multi-Label Classification}, DOI={10.17619/UNIPB/1-1302}, author={Wever, Marcel Dominik}, year={2021} }","mla":"Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification. 2021, doi:10.17619/UNIPB/1-1302.","ama":"Wever MD. Automated Machine Learning for Multi-Label Classification.; 2021. doi:10.17619/UNIPB/1-1302","apa":"Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification. https://doi.org/10.17619/UNIPB/1-1302","chicago":"Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification, 2021. https://doi.org/10.17619/UNIPB/1-1302.","ieee":"M. D. Wever, Automated Machine Learning for Multi-Label Classification. 2021.","short":"M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021."},"type":"dissertation","year":"2021"},{"conference":{"end_date":"2021-05-14","location":"Delhi, India","name":"The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)","start_date":"2021-05-11"},"_id":"21198","date_updated":"2022-08-24T12:49:06Z","series_title":"PAKDD","type":"conference","year":"2021","citation":{"short":"J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).","ieee":"J. M. Hanselle, A. Tornede, M. D. Wever, and E. 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D., Mohr, F., & Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8","ama":"Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. 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Wever, F. Mohr, and E. Hüllermeier, “AutoML for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8."},"year":"2020","doi":"10.1007/978-3-030-66770-2_8","date_updated":"2022-01-06T06:53:11Z","_id":"17424","conference":{"name":"IOTStream Workshop @ ECMLPKDD 2020"}},{"language":[{"iso":"eng"}],"citation":{"short":"S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital Humanities (n.d.).","ieee":"S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction,” Journal of Data Mining and Digital Humanities. episciences.","chicago":"Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities. episciences, n.d.","apa":"Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In Journal of Data Mining and Digital Humanities. episciences.","ama":"Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.","bibtex":"@article{Heid_Wever_Hüllermeier, title={Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction}, journal={Journal of Data Mining and Digital Humanities}, publisher={episciences}, author={Heid, Stefan Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke} }","mla":"Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital Humanities, episciences."},"type":"preprint","year":"2020","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2008.01377"}],"oa":"1","date_updated":"2022-01-06T06:53:15Z","_id":"17605","date_created":"2020-08-05T06:52:53Z","project":[{"_id":"39","name":"InterGramm"}],"status":"public","publication_status":"submitted","publication":"Journal of Data Mining and Digital Humanities","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publisher":"episciences","author":[{"first_name":"Stefan Helmut","orcid":"0000-0002-9461-7372","full_name":"Heid, Stefan Helmut","last_name":"Heid","id":"39640"},{"id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"user_id":"5786","title":"Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction","abstract":[{"text":"Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. \r\nWhile the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography.\r\nThese irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small.\r\nIn our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.","lang":"eng"}]},{"language":[{"iso":"eng"}],"year":"2020","citation":{"apa":"Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.","ama":"Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","mla":"Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","bibtex":"@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020."},"type":"conference","conference":{"location":"Online","name":"Workshop MetaLearn 2020 @ NeurIPS 2020"},"date_updated":"2022-01-06T06:54:26Z","_id":"20306","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2020-11-06T09:42:27Z","status":"public","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publication":"Workshop MetaLearn 2020 @ NeurIPS 2020","author":[{"last_name":"Tornede","id":"38209","first_name":"Alexander","full_name":"Tornede, Alexander"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"user_id":"5786","title":"Towards Meta-Algorithm Selection"},{"status":"public","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2020-08-25T12:09:28Z","author":[{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"last_name":"Werner","first_name":"Stefan","full_name":"Werner, Stefan"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"publication":"ACML 2020","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","user_id":"5786","abstract":[{"lang":"eng","text":"Algorithm selection (AS) deals with the automatic selection of an algorithm\r\nfrom a fixed set of candidate algorithms most suitable for a specific instance\r\nof an algorithmic problem class, where \"suitability\" often refers to an\r\nalgorithm's runtime. Due to possibly extremely long runtimes of candidate\r\nalgorithms, training data for algorithm selection models is usually generated\r\nunder time constraints in the sense that not all algorithms are run to\r\ncompletion on all instances. Thus, training data usually comprises censored\r\ninformation, as the true runtime of algorithms timed out remains unknown.\r\nHowever, many standard AS approaches are not able to handle such information in\r\na proper way. On the other side, survival analysis (SA) naturally supports\r\ncensored data and offers appropriate ways to use such data for learning\r\ndistributional models of algorithm runtime, as we demonstrate in this work. We\r\nleverage such models as a basis of a sophisticated decision-theoretic approach\r\nto algorithm selection, which we dub Run2Survive. Moreover, taking advantage of\r\na framework of this kind, we advocate a risk-averse approach to algorithm\r\nselection, in which the avoidance of a timeout is given high priority. In an\r\nextensive experimental study with the standard benchmark ASlib, our approach is\r\nshown to be highly competitive and in many cases even superior to\r\nstate-of-the-art AS approaches."}],"year":"2020","citation":{"short":"A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.","ieee":"A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,” presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand, 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” In ACML 2020, 2020.","apa":"Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok, Thailand.","ama":"Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020. ; 2020.","mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.","bibtex":"@inproceedings{Tornede_Wever_Werner_Mohr_Hüllermeier_2020, title={Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}, booktitle={ACML 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }"},"type":"conference","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}],"date_updated":"2022-01-06T06:53:28Z","_id":"18276","conference":{"location":"Bangkok, Thailand","start_date":"2020-11-18","name":"12th Asian Conference on Machine Learning","end_date":"2020-11-20"}},{"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2020-01-23T08:44:08Z","status":"public","publication_status":"accepted","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"publisher":"Springer","author":[{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"id":"38209","last_name":"Tornede","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"user_id":"5786","title":"LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification","abstract":[{"lang":"eng","text":"In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance."}],"language":[{"iso":"eng"}],"year":"2020","citation":{"short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification,” presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.","apa":"Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Symposium on Intelligent Data Analysis, Konstanz, Germany.","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.” Springer, n.d.","bibtex":"@inproceedings{Wever_Tornede_Mohr_Hüllermeier, title={LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification}, publisher={Springer}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke} }","mla":"Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. Springer."},"type":"conference","conference":{"location":"Konstanz, Germany","name":"Symposium on Intelligent Data Analysis","start_date":"2020-04-24","end_date":"2020-04-27"},"date_updated":"2022-01-06T06:52:30Z","_id":"15629"},{"abstract":[{"text":"In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.","lang":"eng"}],"user_id":"15415","publication":"Evolutionary Computation","publisher":"MIT Press Journals","author":[{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"first_name":"Lorijn","full_name":"van Rooijen, Lorijn","last_name":"van Rooijen","id":"58843"},{"first_name":"Heiko","full_name":"Hamann, Heiko","last_name":"Hamann"}],"volume":28,"date_created":"2019-11-18T14:19:19Z","status":"public","_id":"15025","intvolume":" 28","issue":"2","page":"165–193","type":"journal_article","year":"2020","citation":{"bibtex":"@article{Wever_van Rooijen_Hamann_2020, title={Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets}, volume={28}, DOI={10.1162/evco_a_00266}, number={2}, journal={Evolutionary Computation}, publisher={MIT Press Journals}, author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2020}, pages={165–193} }","mla":"Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266.","chicago":"Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation 28, no. 2 (2020): 165–193. https://doi.org/10.1162/evco_a_00266.","apa":"Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266","ama":"Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266","ieee":"M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets,” Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi: 10.1162/evco_a_00266.","short":"M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193."},"title":"Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets","related_material":{"link":[{"relation":"confirmation","url":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266"}]},"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"},{"_id":"63"},{"_id":"238"}],"publication_status":"published","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"9","name":"SFB 901 - Subproject B1"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_updated":"2022-01-06T06:52:15Z","doi":"10.1162/evco_a_00266","language":[{"iso":"eng"}]}]