{"user_id":"5786","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"citation":{"short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.","apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>.","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Published online 2021:1-1. doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, pp. 1–1, doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, pp. 1–1, 2021, doi: <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>}, 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} }"},"title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation","_id":"21004","year":"2021","page":"1-1","date_created":"2021-01-16T14:48:13Z","date_updated":"2022-01-06T06:54:42Z","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"}],"publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"type":"journal_article","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"author":[{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever"},{"last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209","first_name":"Alexander"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"status":"public","language":[{"iso":"eng"}],"abstract":[{"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.","lang":"eng"}],"publication_status":"published","doi":"10.1109/tpami.2021.3051276"}