[{"ddc":["006"],"user_id":"49109","_id":"3852","urn":"38527","has_accepted_license":"1","conference":{"name":"ICML 2018 AutoML Workshop","start_date":"2018-07-10","location":"Stockholm, Sweden","end_date":"2018-07-15"},"status":"public","oa":"1","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"quality_controlled":"1","citation":{"mla":"Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” <i>ICML 2018 AutoML Workshop</i>, 2018.","ama":"Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: <i>ICML 2018 AutoML Workshop</i>. ; 2018.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In <i>ICML 2018 AutoML Workshop</i>. Stockholm, Sweden.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in <i>ICML 2018 AutoML Workshop</i>, Stockholm, Sweden, 2018.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In <i>ICML 2018 AutoML Workshop</i>, 2018."},"file_date_updated":"2018-08-09T06:14:43Z","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"date_updated":"2022-01-06T06:59:46Z","author":[{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"year":"2018","title":"ML-Plan for Unlimited-Length Machine Learning Pipelines","department":[{"_id":"355"}],"keyword":["automated machine learning","complex pipelines","hierarchical planning"],"type":"conference","date_created":"2018-08-09T06:14:54Z","file":[{"content_type":"application/pdf","file_id":"3853","date_updated":"2018-08-09T06:14:43Z","relation":"main_file","file_size":297811,"access_level":"open_access","file_name":"38.pdf","date_created":"2018-08-09T06:14:43Z","creator":"wever"}],"abstract":[{"text":"In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.\r\nVarious AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.\r\nMore specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.\r\nIn this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.\r\nWe evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.","lang":"eng"}],"publication":"ICML 2018 AutoML Workshop"}]