[{"title":"AutoML for Predictive Maintenance: One Tool to RUL Them All","conference":{"name":"IOTStream Workshop @ ECMLPKDD 2020"},"doi":"10.1007/978-3-030-66770-2_8","date_updated":"2022-01-06T06:53:11Z","date_created":"2020-07-28T09:17:41Z","author":[{"first_name":"Tanja","id":"40795","full_name":"Tornede, Tanja","last_name":"Tornede"},{"first_name":"Alexander","last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"year":"2020","citation":{"ama":"Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive Maintenance: One Tool to RUL Them All. In: <i>Proceedings of the ECMLPKDD 2020</i>. ; 2020. doi:<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>","chicago":"Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” In <i>Proceedings of the ECMLPKDD 2020</i>, 2020. <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">https://doi.org/10.1007/978-3-030-66770-2_8</a>.","ieee":"T. Tornede, A. Tornede, M. D. 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: <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>.","apa":"Tornede, T., Tornede, A., Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2020). AutoML for Predictive Maintenance: One Tool to RUL Them All. <i>Proceedings of the ECMLPKDD 2020</i>. IOTStream Workshop @ ECMLPKDD 2020. <a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">https://doi.org/10.1007/978-3-030-66770-2_8</a>","bibtex":"@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML for Predictive Maintenance: One Tool to RUL Them All}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>}, booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }","mla":"Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL Them All.” <i>Proceedings of the ECMLPKDD 2020</i>, 2020, doi:<a href=\"https://doi.org/10.1007/978-3-030-66770-2_8\">10.1007/978-3-030-66770-2_8</a>.","short":"T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the ECMLPKDD 2020, 2020."},"language":[{"iso":"eng"}],"_id":"17424","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"SFB 901","_id":"1"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","status":"public","publication":"Proceedings of the ECMLPKDD 2020","type":"conference"},{"year":"2020","citation":{"apa":"Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. Workshop MetaLearn 2020 @ NeurIPS 2020, Online.","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.","mla":"Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020.","ama":"Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. ; 2020.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020."},"title":"Towards Meta-Algorithm Selection","conference":{"location":"Online","name":"Workshop MetaLearn 2020 @ NeurIPS 2020"},"date_updated":"2022-01-06T06:54:26Z","author":[{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"full_name":"Wever, Marcel Dominik","id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2020-11-06T09:42:27Z","status":"public","publication":"Workshop MetaLearn 2020 @ NeurIPS 2020","type":"conference","language":[{"iso":"eng"}],"_id":"20306","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"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786"},{"language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","_id":"18276","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"status":"public","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."}],"publication":"ACML 2020","type":"conference","conference":{"end_date":"2020-11-20","location":"Bangkok, Thailand","name":"12th Asian Conference on Machine Learning","start_date":"2020-11-18"},"main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}],"title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","author":[{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"full_name":"Werner, Stefan","last_name":"Werner","first_name":"Stefan"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2020-08-25T12:09:28Z","date_updated":"2022-01-06T06:53:28Z","citation":{"ama":"Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In: <i>ACML 2020</i>. ; 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 <i>ACML 2020</i>, 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.","mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” <i>ACML 2020</i>, 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} }","short":"A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020, 2020.","apa":"Tornede, A., Wever, M. D., Werner, S., Mohr, F., &#38; Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. <i>ACML 2020</i>. 12th Asian Conference on Machine Learning, Bangkok, Thailand."},"year":"2020"},{"status":"public","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."}],"type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","_id":"15629","project":[{"_id":"1","name":"SFB 901"},{"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"}],"citation":{"short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: 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. <i>LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification</i>. Springer.","apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (n.d.). <i>LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification</i>. 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.","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.","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."},"year":"2020","publication_status":"accepted","conference":{"start_date":"2020-04-24","name":"Symposium on Intelligent Data Analysis","location":"Konstanz, Germany","end_date":"2020-04-27"},"title":"LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification","author":[{"id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"first_name":"Alexander","last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"date_created":"2020-01-23T08:44:08Z","date_updated":"2022-01-06T06:52:30Z","publisher":"Springer"},{"abstract":[{"lang":"eng","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."}],"status":"public","type":"journal_article","publication":"Evolutionary Computation","language":[{"iso":"eng"}],"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"9","name":"SFB 901 - Subproject B1"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"_id":"15025","user_id":"15415","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"},{"_id":"63"},{"_id":"238"}],"year":"2020","citation":{"ama":"Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. <i>Evolutionary Computation</i>. 2020;28(2):165–193. doi:<a href=\"https://doi.org/10.1162/evco_a_00266\">10.1162/evco_a_00266</a>","ieee":"M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets,” <i>Evolutionary Computation</i>, vol. 28, no. 2, pp. 165–193, 2020, doi: <a href=\"https://doi.org/10.1162/evco_a_00266\">10.1162/evco_a_00266</a>.","chicago":"Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” <i>Evolutionary Computation</i> 28, no. 2 (2020): 165–193. <a href=\"https://doi.org/10.1162/evco_a_00266\">https://doi.org/10.1162/evco_a_00266</a>.","mla":"Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.” <i>Evolutionary Computation</i>, vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:<a href=\"https://doi.org/10.1162/evco_a_00266\">10.1162/evco_a_00266</a>.","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={<a href=\"https://doi.org/10.1162/evco_a_00266\">10.1162/evco_a_00266</a>}, 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} }","short":"M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193.","apa":"Wever, M. D., van Rooijen, L., &#38; Hamann, H. (2020). Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. <i>Evolutionary Computation</i>, <i>28</i>(2), 165–193. <a href=\"https://doi.org/10.1162/evco_a_00266\">https://doi.org/10.1162/evco_a_00266</a>"},"page":"165–193","intvolume":"        28","publication_status":"published","issue":"2","related_material":{"link":[{"relation":"confirmation","url":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266"}]},"title":"Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets","doi":"10.1162/evco_a_00266","publisher":"MIT Press Journals","date_updated":"2022-01-06T06:52:15Z","date_created":"2019-11-18T14:19:19Z","author":[{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik"},{"first_name":"Lorijn","last_name":"van Rooijen","id":"58843","full_name":"van Rooijen, Lorijn"},{"first_name":"Heiko","last_name":"Hamann","full_name":"Hamann, Heiko"}],"volume":28},{"department":[{"_id":"574"}],"user_id":"477","_id":"16935","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"language":[{"iso":"eng"}],"type":"dissertation","status":"public","date_created":"2020-05-05T06:16:29Z","author":[{"last_name":"Moussalem","full_name":"Moussalem, Diego Campos","first_name":"Diego Campos"}],"supervisor":[{"id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"}],"date_updated":"2022-11-30T13:43:53Z","doi":"10.17619/UNIPB/1-980","title":"Knowledge Graphs for Multilingual Language Translation and Generation","citation":{"apa":"Moussalem, D. C. (2020). <i>Knowledge Graphs for Multilingual Language Translation and Generation</i>. <a href=\"https://doi.org/10.17619/UNIPB/1-980\">https://doi.org/10.17619/UNIPB/1-980</a>","bibtex":"@book{Moussalem_2020, title={Knowledge Graphs for Multilingual Language Translation and Generation}, DOI={<a href=\"https://doi.org/10.17619/UNIPB/1-980\">10.17619/UNIPB/1-980</a>}, author={Moussalem, Diego Campos}, year={2020} }","mla":"Moussalem, Diego Campos. <i>Knowledge Graphs for Multilingual Language Translation and Generation</i>. 2020, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-980\">10.17619/UNIPB/1-980</a>.","short":"D.C. Moussalem, Knowledge Graphs for Multilingual Language Translation and Generation, 2020.","chicago":"Moussalem, Diego Campos. <i>Knowledge Graphs for Multilingual Language Translation and Generation</i>, 2020. <a href=\"https://doi.org/10.17619/UNIPB/1-980\">https://doi.org/10.17619/UNIPB/1-980</a>.","ieee":"D. C. Moussalem, <i>Knowledge Graphs for Multilingual Language Translation and Generation</i>. 2020.","ama":"Moussalem DC. <i>Knowledge Graphs for Multilingual Language Translation and Generation</i>.; 2020. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-980\">10.17619/UNIPB/1-980</a>"},"year":"2020"},{"publisher":"Springer","date_created":"2019-10-10T13:41:06Z","title":"A Case for a New IT Ecosystem: On-The-Fly Computing","issue":"6","year":"2020","ddc":["004"],"language":[{"iso":"eng"}],"publication":"Business & Information Systems Engineering","file":[{"relation":"main_file","success":1,"content_type":"application/pdf","access_level":"closed","file_id":"15311","file_name":"Karl2019_Article_ACaseForANewITEcosystemOn-The-.pdf","file_size":454532,"date_created":"2019-12-12T10:24:47Z","creator":"ups","date_updated":"2019-12-12T10:24:47Z"}],"date_updated":"2022-12-02T09:27:17Z","volume":62,"author":[{"first_name":"Holger","full_name":"Karl, Holger","id":"126","last_name":"Karl"},{"id":"21117","full_name":"Kundisch, Dennis","last_name":"Kundisch","first_name":"Dennis"},{"last_name":"Meyer auf der Heide","full_name":"Meyer auf der Heide, Friedhelm","id":"15523","first_name":"Friedhelm"},{"first_name":"Heike","last_name":"Wehrheim","id":"573","full_name":"Wehrheim, Heike"}],"doi":"10.1007/s12599-019-00627-x","has_accepted_license":"1","publication_status":"published","intvolume":"        62","page":"467-481","citation":{"ama":"Karl H, Kundisch D, Meyer auf der Heide F, Wehrheim H. A Case for a New IT Ecosystem: On-The-Fly Computing. <i>Business &#38; Information Systems Engineering</i>. 2020;62(6):467-481. doi:<a href=\"https://doi.org/10.1007/s12599-019-00627-x\">10.1007/s12599-019-00627-x</a>","ieee":"H. Karl, D. Kundisch, F. Meyer auf der Heide, and H. Wehrheim, “A Case for a New IT Ecosystem: On-The-Fly Computing,” <i>Business &#38; Information Systems Engineering</i>, vol. 62, no. 6, pp. 467–481, 2020, doi: <a href=\"https://doi.org/10.1007/s12599-019-00627-x\">10.1007/s12599-019-00627-x</a>.","chicago":"Karl, Holger, Dennis Kundisch, Friedhelm Meyer auf der Heide, and Heike Wehrheim. “A Case for a New IT Ecosystem: On-The-Fly Computing.” <i>Business &#38; Information Systems Engineering</i> 62, no. 6 (2020): 467–81. <a href=\"https://doi.org/10.1007/s12599-019-00627-x\">https://doi.org/10.1007/s12599-019-00627-x</a>.","bibtex":"@article{Karl_Kundisch_Meyer auf der Heide_Wehrheim_2020, title={A Case for a New IT Ecosystem: On-The-Fly Computing}, volume={62}, DOI={<a href=\"https://doi.org/10.1007/s12599-019-00627-x\">10.1007/s12599-019-00627-x</a>}, number={6}, journal={Business &#38; Information Systems Engineering}, publisher={Springer}, author={Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and Wehrheim, Heike}, year={2020}, pages={467–481} }","mla":"Karl, Holger, et al. “A Case for a New IT Ecosystem: On-The-Fly Computing.” <i>Business &#38; Information Systems Engineering</i>, vol. 62, no. 6, Springer, 2020, pp. 467–81, doi:<a href=\"https://doi.org/10.1007/s12599-019-00627-x\">10.1007/s12599-019-00627-x</a>.","short":"H. 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Bonn: Gesellschaft für Informatik e.V.","bibtex":"@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }","mla":"Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” <i>INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft</i>, Gesellschaft für Informatik e.V., 2019, pp. 273–74.","short":"F. Mohr, M.D. Wever, A. Tornede, E. 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Mohr, A. Tornede, E. Hüllermeier, in: 2019.","apa":"Wever, M. D., Mohr, F., Tornede, A., &#38; Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.","ama":"Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.","chicago":"Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019.","ieee":"M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019."},"has_accepted_license":"1","ddc":["006"],"language":[{"iso":"eng"}],"file_date_updated":"2019-09-10T08:20:44Z","_id":"10232","project":[{"_id":"1","name":"SFB 901"},{"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"}],"department":[{"_id":"355"}],"user_id":"33176","abstract":[{"text":"Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. 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San Francisco, CA, USA: IEEE, 2018. <a href=\"https://doi.org/10.1109/SCC.2018.00039\">https://doi.org/10.1109/SCC.2018.00039</a>.","ieee":"F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in <i>SCC</i>, San Francisco, CA, USA, 2018.","ama":"Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: <i>SCC</i>. San Francisco, CA, USA: IEEE; 2018. doi:<a href=\"https://doi.org/10.1109/SCC.2018.00039\">10.1109/SCC.2018.00039</a>","apa":"Mohr, F., Wever, M. D., Hüllermeier, E., &#38; Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In <i>SCC</i>. San Francisco, CA, USA: IEEE. <a href=\"https://doi.org/10.1109/SCC.2018.00039\">https://doi.org/10.1109/SCC.2018.00039</a>","mla":"Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.” <i>SCC</i>, IEEE, 2018, doi:<a href=\"https://doi.org/10.1109/SCC.2018.00039\">10.1109/SCC.2018.00039</a>.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, A. 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Delft, Netherlands: AAAI.","mla":"Mohr, Felix, et al. “Programmatic Task Network Planning.” <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i>, AAAI, 2018, pp. 31–39.","bibtex":"@inproceedings{Mohr_Lettmann_Hüllermeier_Wever_2018, title={Programmatic Task Network Planning}, booktitle={Proceedings of the 1st ICAPS Workshop on Hierarchical Planning}, publisher={AAAI}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke and Wever, Marcel Dominik}, year={2018}, pages={31–39} }","short":"F. Mohr, T. Lettmann, E. 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Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In <i>SCC</i>. San Francisco, CA, USA: IEEE Computer Society. <a href=\"https://doi.org/10.1109/SCC.2018.00036\">https://doi.org/10.1109/SCC.2018.00036</a>","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service Construction with Prototypes.” In <i>SCC</i>. San Francisco, CA, USA: IEEE Computer Society, 2018. <a href=\"https://doi.org/10.1109/SCC.2018.00036\">https://doi.org/10.1109/SCC.2018.00036</a>.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in <i>SCC</i>, San Francisco, CA, USA, 2018.","ama":"Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: <i>SCC</i>. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:<a href=\"https://doi.org/10.1109/SCC.2018.00036\">10.1109/SCC.2018.00036</a>"},"place":"San Francisco, CA, USA","author":[{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_updated":"2022-01-06T06:56:32Z","oa":"1","main_file_link":[{"open_access":"1","url":"https://ieeexplore.ieee.org/abstract/document/8456422"}],"doi":"10.1109/SCC.2018.00036","conference":{"name":"IEEE International Conference on Services Computing, SCC 2018","start_date":"2018-07-02","end_date":"2018-07-07","location":"San Francisco, CA, USA"},"type":"conference","status":"public","user_id":"49109","department":[{"_id":"355"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"_id":"2471","file_date_updated":"2018-11-06T15:15:38Z"},{"main_file_link":[{"url":"https://rdcu.be/3Nc2","open_access":"1"}],"doi":"10.1007/s10994-018-5735-z","conference":{"location":"Dublin, Ireland","end_date":"2018-09-14","start_date":"2018-09-10","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases"},"oa":"1","date_updated":"2022-01-06T06:59:21Z","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"citation":{"bibtex":"@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine Learning via Hierarchical Planning}, DOI={<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>}, journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }","short":"F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.","mla":"Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, Springer, 2018, pp. 1495–515, doi:<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>.","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. <i>Machine Learning</i>, 1495–1515. <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">https://doi.org/10.1007/s10994-018-5735-z</a>","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, 2018, 1495–1515. <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">https://doi.org/10.1007/s10994-018-5735-z</a>.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” <i>Machine Learning</i>, pp. 1495–1515, 2018, doi: <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>.","ama":"Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. <i>Machine Learning</i>. Published online 2018:1495-1515. doi:<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>"},"page":"1495-1515","publication_status":"epub_ahead","has_accepted_license":"1","publication_identifier":{"eissn":["1573-0565"],"issn":["0885-6125"]},"article_type":"original","file_date_updated":"2018-11-02T15:32:16Z","project":[{"_id":"1","name":"SFB 901"},{"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"}],"_id":"3510","user_id":"5786","department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"status":"public","type":"journal_article","title":"ML-Plan: Automated Machine Learning via Hierarchical Planning","publisher":"Springer","date_created":"2018-07-08T14:06:14Z","year":"2018","ddc":["000"],"keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches."}],"file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":1070937,"access_level":"closed","file_name":"ML-PlanAutomatedMachineLearnin.pdf","file_id":"5306","date_updated":"2018-11-02T15:32:16Z","date_created":"2018-11-02T15:32:16Z","creator":"ups"}],"publication":"Machine Learning"},{"file":[{"date_created":"2018-11-06T15:23:02Z","creator":"wever","date_updated":"2018-11-06T15:23:02Z","file_name":"Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf","access_level":"closed","file_id":"5385","file_size":1348768,"content_type":"application/pdf","relation":"main_file","success":1}],"publication":"Proceedings of the Symposium on Intelligent Data Analysis","language":[{"iso":"eng"}],"ddc":["000"],"year":"2018","quality_controlled":"1","title":"Reduction Stumps for Multi-Class Classification","date_created":"2018-07-13T15:29:15Z","status":"public","type":"conference","file_date_updated":"2018-11-06T15:23:02Z","department":[{"_id":"355"}],"user_id":"49109","_id":"3552","project":[{"name":"SFB 901","_id":"1"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"3","name":"SFB 901 - Project Area B"}],"citation":{"ama":"Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: <i>Proceedings of the Symposium on Intelligent Data Analysis</i>. ‘s-Hertogenbosch, the Netherlands. doi:<a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">10.1007/978-3-030-01768-2_19</a>","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class Classification,” in <i>Proceedings of the Symposium on Intelligent Data Analysis</i>, ‘s-Hertogenbosch, the Netherlands.","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps for Multi-Class Classification.” In <i>Proceedings of the Symposium on Intelligent Data Analysis</i>. ‘s-Hertogenbosch, the Netherlands, n.d. <a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">https://doi.org/10.1007/978-3-030-01768-2_19</a>.","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In <i>Proceedings of the Symposium on Intelligent Data Analysis</i>. ‘s-Hertogenbosch, the Netherlands. <a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">https://doi.org/10.1007/978-3-030-01768-2_19</a>","mla":"Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.” <i>Proceedings of the Symposium on Intelligent Data Analysis</i>, doi:<a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">10.1007/978-3-030-01768-2_19</a>.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands}, title={Reduction Stumps for Multi-Class Classification}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">10.1007/978-3-030-01768-2_19</a>}, booktitle={Proceedings of the Symposium on Intelligent Data Analysis}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke} }"},"place":"‘s-Hertogenbosch, the Netherlands","has_accepted_license":"1","publication_status":"accepted","doi":"10.1007/978-3-030-01768-2_19","conference":{"location":"‘s-Hertogenbosch, the Netherlands","end_date":"2018-10-26","start_date":"2018-10-24","name":"Symposium on Intelligent Data Analysis"},"main_file_link":[{"open_access":"1","url":"https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19"}],"author":[{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_updated":"2022-01-06T06:59:25Z","oa":"1"},{"oa":"1","date_updated":"2022-01-06T06:59:46Z","author":[{"full_name":"Wever, Marcel Dominik","id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"conference":{"name":"ICML 2018 AutoML Workshop","start_date":"2018-07-10","end_date":"2018-07-15","location":"Stockholm, Sweden"},"has_accepted_license":"1","citation":{"ama":"Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: <i>ICML 2018 AutoML Workshop</i>. ; 2018.","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.","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.","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.","mla":"Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” <i>ICML 2018 AutoML Workshop</i>, 2018.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 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} }"},"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"_id":"3852","user_id":"49109","department":[{"_id":"355"}],"file_date_updated":"2018-08-09T06:14:43Z","type":"conference","urn":"38527","status":"public","date_created":"2018-08-09T06:14:54Z","title":"ML-Plan for Unlimited-Length Machine Learning Pipelines","quality_controlled":"1","year":"2018","ddc":["006"],"keyword":["automated machine learning","complex pipelines","hierarchical planning"],"language":[{"iso":"eng"}],"publication":"ICML 2018 AutoML Workshop","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"}],"file":[{"date_updated":"2018-08-09T06:14:43Z","date_created":"2018-08-09T06:14:43Z","creator":"wever","file_size":297811,"file_name":"38.pdf","file_id":"3853","access_level":"open_access","content_type":"application/pdf","relation":"main_file"}]},{"type":"conference","status":"public","department":[{"_id":"355"}],"user_id":"33176","_id":"2109","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"}],"file_date_updated":"2018-11-02T14:33:54Z","has_accepted_license":"1","publication_status":"published","citation":{"ama":"Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for Classification. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM; 2018. doi:<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of Evolved Nested Dichotomies for Classification.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM, 2018. <a href=\"https://doi.org/10.1145/3205455.3205562\">https://doi.org/10.1145/3205455.3205562</a>.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies for Classification,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>, Kyoto, Japan, 2018.","apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM. <a href=\"https://doi.org/10.1145/3205455.3205562\">https://doi.org/10.1145/3205455.3205562</a>","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.","mla":"Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for Classification.” <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>, ACM, 2018, doi:<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles of Evolved Nested Dichotomies for Classification}, DOI={<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}, publisher={ACM}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }"},"place":"Kyoto, Japan","author":[{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"date_updated":"2022-01-06T06:54:45Z","oa":"1","doi":"10.1145/3205455.3205562","conference":{"name":"GECCO 2018","start_date":"2018-07-15","end_date":"2018-07-19","location":"Kyoto, Japan"},"main_file_link":[{"url":"https://dl.acm.org/citation.cfm?doid=3205455.3205562","open_access":"1"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018","file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":875404,"access_level":"closed","file_id":"5275","file_name":"p561-wever.pdf","date_updated":"2018-11-02T14:33:54Z","date_created":"2018-11-02T14:33:54Z","creator":"ups"}],"abstract":[{"text":"In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance.","lang":"eng"}],"language":[{"iso":"eng"}],"keyword":["Classification","Hierarchical Decomposition","Indirect Encoding"],"ddc":["000"],"year":"2018","date_created":"2018-03-31T13:51:23Z","publisher":"ACM","title":"Ensembles of Evolved Nested Dichotomies for Classification"},{"title":"Automated Multi-Label Classification based on ML-Plan","main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/1811.04060.pdf"}],"publisher":"Arxiv","oa":"1","date_updated":"2022-01-06T06:53:17Z","author":[{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"date_created":"2020-08-07T11:38:10Z","year":"2018","citation":{"apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). <i>Automated Multi-Label Classification based on ML-Plan</i>. Arxiv.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, (2018).","bibtex":"@article{Wever_Mohr_Hüllermeier_2018, title={Automated Multi-Label Classification based on ML-Plan}, publisher={Arxiv}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","mla":"Wever, Marcel Dominik, et al. <i>Automated Multi-Label Classification Based on ML-Plan</i>. Arxiv, 2018.","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.","ama":"Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. Published online 2018."},"language":[{"iso":"eng"}],"_id":"17713","project":[{"name":"SFB 901","_id":"1"},{"_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"}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","status":"public","type":"preprint"}]