[{"author":[{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176"},{"id":"58843","full_name":"van Rooijen, Lorijn","last_name":"van Rooijen","first_name":"Lorijn"},{"full_name":"Hamann, Heiko","last_name":"Hamann","first_name":"Heiko"}],"title":"Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets","year":"2020","intvolume":"        28","publication_status":"published","date_updated":"2022-01-06T06:52:15Z","language":[{"iso":"eng"}],"doi":"10.1162/evco_a_00266","issue":"2","publication":"Evolutionary Computation","related_material":{"link":[{"relation":"confirmation","url":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266"}]},"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"}],"date_created":"2019-11-18T14:19:19Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"},{"_id":"63"},{"_id":"238"}],"type":"journal_article","status":"public","_id":"15025","publisher":"MIT Press Journals","page":"165–193","volume":28,"user_id":"15415","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={<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} }","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>","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>.","short":"M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020) 165–193.","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>.","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>.","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>"},"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"9","name":"SFB 901 - Subproject B1"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}]},{"user_id":"5786","_id":"17565","language":[{"iso":"ger"}],"page":"124-146","publication_status":"published","date_updated":"2022-01-06T06:53:15Z","author":[{"full_name":"Merten, Marie-Luis","first_name":"Marie-Luis","last_name":"Merten"},{"first_name":"Nina","last_name":"Seemann","full_name":"Seemann, Nina"},{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"}],"title":"Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff","status":"public","year":"2019","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"journal_article","date_created":"2020-08-03T13:55:04Z","project":[{"_id":"39","name":"InterGramm"}],"citation":{"ieee":"M.-L. Merten, N. Seemann, and M. D. Wever, “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff,” <i>Niederdeutsches Jahrbuch</i>, no. 142, pp. 124–146, 2019.","apa":"Merten, M.-L., Seemann, N., &#38; Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. <i>Niederdeutsches Jahrbuch</i>, <i>142</i>, 124–146.","short":"M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146.","chicago":"Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel digital-kulturwissenschaftlich erforscht. 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Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. <i>Niederdeutsches Jahrbuch</i>. 2019;(142):124-146."},"publication":"Niederdeutsches Jahrbuch","issue":"142"},{"file_date_updated":"2019-04-10T07:17:17Z","citation":{"ieee":"M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine Learning for Multi-Label Classification,” presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany, 2019.","mla":"Wever, Marcel Dominik, et al. <i>Towards Automated Machine Learning for Multi-Label Classification</i>. 2019.","apa":"Wever, M. D., Mohr, F., Hüllermeier, E., &#38; Hetzer, A. (2019). Towards Automated Machine Learning for Multi-Label Classification. 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November 2019</i>, Dortmund, 2019, pp. 135–146.","apa":"Tornede, A., Wever, M. D., &#38; Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, &#38; R. Mikut (Eds.), <i>Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019</i> (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In <i>Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019</i>, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, 135–46. KIT Scientific Publishing, Karlsruhe, 2019.","mla":"Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” <i>Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019</i>, edited by Frank Hoffmann et al., KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–46.","bibtex":"@inproceedings{Tornede_Wever_Hüllermeier_2019, title={Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking}, booktitle={Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019}, publisher={KIT Scientific Publishing, Karlsruhe}, author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Hoffmann, Frank and Hüllermeier, Eyke and Mikut, RalfEditors}, year={2019}, pages={135–146} }","ama":"Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut R, eds. <i>Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019</i>. KIT Scientific Publishing, Karlsruhe; 2019:135-146."},"oa":"1","date_updated":"2022-01-06T06:52:14Z","publication_status":"published","title":"Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking","year":"2019","author":[{"id":"38209","first_name":"Alexander","last_name":"Tornede","full_name":"Tornede, Alexander"},{"id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"publication_identifier":{"isbn":["978-3-7315-0979-0"]},"language":[{"iso":"eng"}],"publication":"Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019","type":"conference","department":[{"_id":"355"}],"file":[{"creator":"ahetzer","date_created":"2020-05-25T08:01:31Z","date_updated":"2020-05-25T08:01:31Z","relation":"main_file","file_size":468825,"access_level":"open_access","file_name":"ci_workshop_tornede.pdf","content_type":"application/pdf","file_id":"17060"}],"date_created":"2019-11-15T13:29:25Z"},{"department":[{"_id":"355"}],"type":"conference_abstract","place":"Bonn","date_created":"2019-09-04T08:44:46Z","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"citation":{"ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: <i>INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft</i>. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.","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.","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “From Automated to On-The-Fly Machine Learning.” In <i>INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft</i>, 273–74. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft für Informatik e.V., 2019.","short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., Bonn, 2019, pp. 273–274.","apa":"Mohr, F., Wever, M. D., Tornede, A., &#38; Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In <i>INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft</i> (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.","ieee":"F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in <i>INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft</i>, Kassel, 2019, pp. 273–274."},"publication":"INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft","user_id":"38209","_id":"13132","series_title":"INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik","publisher":"Gesellschaft für Informatik e.V.","language":[{"iso":"eng"}],"page":" 273-274 ","date_updated":"2022-01-06T06:51:28Z","conference":{"start_date":"2019-09-23","name":"Informatik 2019","location":"Kassel","end_date":"2019-09-26"},"author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever"},{"id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede","first_name":"Alexander"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"title":"From Automated to On-The-Fly Machine Learning","year":"2019","status":"public"},{"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. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with.","lang":"eng"}],"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"}],"file_date_updated":"2019-09-10T08:20:44Z","citation":{"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.","mla":"Wever, Marcel Dominik, et al. <i>Automating Multi-Label Classification Extending ML-Plan</i>. 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.","bibtex":"@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }","short":"M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.","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."},"type":"conference","oa":"1","department":[{"_id":"355"}],"file":[{"date_created":"2019-09-10T08:19:01Z","creator":"wever","content_type":"application/pdf","file_id":"13177","date_updated":"2019-09-10T08:20:44Z","relation":"main_file","file_size":388191,"access_level":"open_access","file_name":"Automating_MultiLabel_Classification_Extending_ML-Plan.pdf"}],"date_created":"2019-06-11T21:33:06Z","date_updated":"2022-01-06T06:50:33Z","has_accepted_license":"1","year":"2019","title":"Automating Multi-Label Classification Extending ML-Plan","status":"public","conference":{"location":"Long Beach, CA, USA","name":"6th ICML Workshop on Automated Machine Learning (AutoML 2019)","start_date":"2019-06-09","end_date":"2019-06-15"},"author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander","last_name":"Tornede"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"ddc":["006"],"user_id":"33176","_id":"10232","language":[{"iso":"eng"}]},{"oa":"1","place":"San Francisco, CA, USA","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"citation":{"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>.","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>","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco, CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services}, DOI={<a href=\"https://doi.org/10.1109/SCC.2018.00039\">10.1109/SCC.2018.00039</a>}, booktitle={SCC}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke and Faez, Amin}, year={2018} }","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>","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.","chicago":"Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP) Towards the Automated Composition of Machine Learning Services.” In <i>SCC</i>. 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>.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, A. 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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>.","mla":"Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” <i>SCC</i>, IEEE Computer Society, 2018, doi:<a href=\"https://doi.org/10.1109/SCC.2018.00036\">10.1109/SCC.2018.00036</a>.","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA}, title={On-The-Fly Service Construction with Prototypes}, DOI={<a href=\"https://doi.org/10.1109/SCC.2018.00036\">10.1109/SCC.2018.00036</a>}, booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }","ama":"Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: <i>SCC</i>. 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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.","lang":"eng"}],"file":[{"date_created":"2018-11-02T15:32:16Z","creator":"ups","success":1,"content_type":"application/pdf","file_id":"5306","date_updated":"2018-11-02T15:32:16Z","relation":"main_file","access_level":"closed","file_size":1070937,"file_name":"ML-PlanAutomatedMachineLearnin.pdf"}],"date_created":"2018-07-08T14:06:14Z","keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"type":"journal_article","department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"title":"ML-Plan: Automated Machine Learning via Hierarchical Planning","year":"2018","publication_identifier":{"eissn":["1573-0565"],"issn":["0885-6125"]},"author":[{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"date_updated":"2022-01-06T06:59:21Z","publication_status":"epub_ahead","article_type":"original","main_file_link":[{"open_access":"1","url":"https://rdcu.be/3Nc2"}],"language":[{"iso":"eng"}],"doi":"10.1007/s10994-018-5735-z","file_date_updated":"2018-11-02T15:32:16Z","citation":{"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>","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>.","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>","short":"F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.","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>.","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} }"},"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"oa":"1","status":"public","conference":{"location":"Dublin, Ireland","start_date":"2018-09-10","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","end_date":"2018-09-14"},"has_accepted_license":"1","page":"1495-1515","publisher":"Springer","_id":"3510","ddc":["000"],"user_id":"5786"},{"doi":"10.1007/978-3-030-01768-2_19","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19","open_access":"1"}],"publication_status":"accepted","date_updated":"2022-01-06T06:59:25Z","author":[{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever","id":"33176"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"year":"2018","title":"Reduction Stumps for Multi-Class Classification","department":[{"_id":"355"}],"type":"conference","date_created":"2018-07-13T15:29:15Z","file":[{"date_created":"2018-11-06T15:23:02Z","creator":"wever","success":1,"content_type":"application/pdf","file_id":"5385","date_updated":"2018-11-06T15:23:02Z","relation":"main_file","file_size":1348768,"access_level":"closed","file_name":"Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf"}],"publication":"Proceedings of the Symposium on Intelligent Data Analysis","user_id":"49109","ddc":["000"],"_id":"3552","has_accepted_license":"1","conference":{"location":"‘s-Hertogenbosch, the Netherlands","name":"Symposium on Intelligent Data Analysis","start_date":"2018-10-24","end_date":"2018-10-26"},"status":"public","oa":"1","place":"‘s-Hertogenbosch, the Netherlands","project":[{"name":"SFB 901","_id":"1"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"SFB 901 - Project Area B","_id":"3"}],"quality_controlled":"1","citation":{"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>.","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} }","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.","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>","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>.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d."},"file_date_updated":"2018-11-06T15:23:02Z"},{"date_updated":"2022-01-06T06:59:46Z","author":[{"id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","full_name":"Wever, Marcel Dominik"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"year":"2018","title":"ML-Plan for Unlimited-Length Machine Learning Pipelines","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"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","department":[{"_id":"355"}],"type":"conference","keyword":["automated machine learning","complex pipelines","hierarchical planning"],"date_created":"2018-08-09T06:14:54Z","file":[{"date_created":"2018-08-09T06:14:43Z","creator":"wever","file_id":"3853","content_type":"application/pdf","relation":"main_file","date_updated":"2018-08-09T06:14:43Z","file_name":"38.pdf","access_level":"open_access","file_size":297811}],"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","user_id":"49109","ddc":["006"],"_id":"3852","urn":"38527","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"quality_controlled":"1","citation":{"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.","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.","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.","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} }","ama":"Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. 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Kyoto, Japan: ACM, 2018. <a href=\"https://doi.org/10.1145/3205455.3205562\">https://doi.org/10.1145/3205455.3205562</a>.","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>","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.","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>","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} }","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>."},"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"}],"place":"Kyoto, Japan","oa":"1","title":"Ensembles of Evolved Nested Dichotomies for Classification","year":"2018","author":[{"last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","id":"33176"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"}],"publication_status":"published","date_updated":"2022-01-06T06:54:45Z","main_file_link":[{"open_access":"1","url":"https://dl.acm.org/citation.cfm?doid=3205455.3205562"}],"language":[{"iso":"eng"}],"doi":"10.1145/3205455.3205562","publication":"Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018","abstract":[{"lang":"eng","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."}],"file":[{"date_created":"2018-11-02T14:33:54Z","creator":"ups","content_type":"application/pdf","success":1,"file_id":"5275","date_updated":"2018-11-02T14:33:54Z","relation":"main_file","file_size":875404,"access_level":"closed","file_name":"p561-wever.pdf"}],"date_created":"2018-03-31T13:51:23Z","type":"conference","keyword":["Classification","Hierarchical Decomposition","Indirect Encoding"],"department":[{"_id":"355"}]},{"oa":"1","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"preprint","date_created":"2020-08-07T11:38:10Z","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"}],"citation":{"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} }","ama":"Wever MD, Mohr F, Hüllermeier E. 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Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft}, author={Seemann, Nina and Geierhos, Michaela and Merten, Marie-Luis and Tophinke, Doris and Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Eckart, Kerstin  and Schlechtweg, Dominik }, year={2018} }","ama":"Seemann N, Geierhos M, Merten M-L, Tophinke D, Wever MD, Hüllermeier E. Supporting the Cognitive Process in Annotation Tasks. In: Eckart K, Schlechtweg D, eds. <i>Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft</i>. ; 2018.","ieee":"N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M. D. Wever, and E. Hüllermeier, “Supporting the Cognitive Process in Annotation Tasks,” in <i>Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft</i>, Stuttgart, Germany, 2018.","apa":"Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., &#38; Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart &#38; D. Schlechtweg (Eds.), <i>Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft</i>.","short":"N. Seemann, M. Geierhos, M.-L. Merten, D. Tophinke, M.D. Wever, E. Hüllermeier, in: K. Eckart, D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, 2018.","chicago":"Seemann, Nina, Michaela Geierhos, Marie-Luis Merten, Doris Tophinke, Marcel Dominik Wever, and Eyke Hüllermeier. “Supporting the Cognitive Process in Annotation Tasks.” In <i>Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft</i>, edited by Kerstin  Eckart and Dominik  Schlechtweg, 2018."},"quality_controlled":"1","project":[{"name":"InterGramm","_id":"39"}],"oa":"1"},{"year":"2017","title":"Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization","author":[{"full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"date_updated":"2022-01-06T06:51:09Z","publication_status":"published","main_file_link":[{"open_access":"1","url":"https://publikationen.bibliothek.kit.edu/1000074341/4643874"}],"language":[{"iso":"eng"}],"publication":"27th Workshop Computational Intelligence","abstract":[{"lang":"eng","text":"These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings."}],"file":[{"date_created":"2018-11-06T15:28:09Z","creator":"wever","file_id":"5387","success":1,"content_type":"application/pdf","file_name":"CI Workshop AutoML.pdf","access_level":"closed","file_size":323589,"relation":"main_file","date_updated":"2018-11-06T15:28:09Z"}],"date_created":"2018-02-22T07:19:18Z","type":"conference","department":[{"_id":"355"}],"status":"public","conference":{"end_date":"2017-11-24","location":"Dortmund","name":"27th Workshop Computational Intelligence","start_date":"2017-11-23"},"has_accepted_license":"1","_id":"1180","ddc":["000"],"user_id":"49109","file_date_updated":"2018-11-06T15:28:09Z","citation":{"apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In <i>27th Workshop Computational Intelligence</i>. Dortmund.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in <i>27th Workshop Computational Intelligence</i>, Dortmund, 2017.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence, Dortmund, 2017.","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” In <i>27th Workshop Computational Intelligence</i>. Dortmund, 2017.","mla":"Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” <i>27th Workshop Computational Intelligence</i>, 2017.","ama":"Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In: <i>27th Workshop Computational Intelligence</i>. Dortmund; 2017.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2017, place={Dortmund}, title={Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}, booktitle={27th Workshop Computational Intelligence}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2017} }"},"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"place":"Dortmund","oa":"1"},{"oa":"1","type":"mastersthesis","date_created":"2017-10-17T12:41:14Z","file":[{"date_updated":"2020-07-16T11:53:45Z","relation":"main_file","access_level":"open_access","file_size":4012186,"file_name":"MT-export-2017-03-17.pdf","content_type":"application/pdf","file_id":"5388","creator":"wever","date_created":"2018-11-06T15:31:48Z"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Subprojekt B1","_id":"9"},{"name":"SFB 901 - Project Area B","_id":"3"}],"citation":{"ieee":"M. D. Wever, <i>Active Learning of User Requirement Specifications in Dynamic Software Service Markets</i>. Universität Paderborn, 2017.","apa":"Wever, M. D. (2017). <i>Active Learning of User Requirement Specifications in Dynamic Software Service Markets</i>. Universität Paderborn.","short":"M.D. Wever, Active Learning of User Requirement Specifications in Dynamic Software Service Markets, Universität Paderborn, 2017.","chicago":"Wever, Marcel Dominik. <i>Active Learning of User Requirement Specifications in Dynamic Software Service Markets</i>. Universität Paderborn, 2017.","mla":"Wever, Marcel Dominik. <i>Active Learning of User Requirement Specifications in Dynamic Software Service Markets</i>. Universität Paderborn, 2017.","bibtex":"@book{Wever_2017, title={Active Learning of User Requirement Specifications in Dynamic Software Service Markets}, publisher={Universität Paderborn}, author={Wever, Marcel Dominik}, year={2017} }","ama":"Wever MD. <i>Active Learning of User Requirement Specifications in Dynamic Software Service Markets</i>. Universität Paderborn; 2017."},"file_date_updated":"2020-07-16T11:53:45Z","ddc":["000"],"user_id":"33176","language":[{"iso":"eng"}],"_id":"119","publisher":"Universität Paderborn","has_accepted_license":"1","date_updated":"2022-01-06T06:51:12Z","author":[{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"}],"status":"public","title":"Active Learning of User Requirement Specifications in Dynamic Software Service Markets","year":"2017"},{"citation":{"chicago":"Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Active Coevolutionary Learning of Requirements Specifications from Examples.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)</i>, 1327--1334, 2017. <a href=\"https://doi.org/10.1145/3071178.3071258\">https://doi.org/10.1145/3071178.3071258</a>.","short":"M.D. Wever, L. van Rooijen, H. Hamann, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2017, pp. 1327--1334.","apa":"Wever, M. D., van Rooijen, L., &#38; Hamann, H. (2017). Active Coevolutionary Learning of Requirements Specifications from Examples. In <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)</i> (pp. 1327--1334). <a href=\"https://doi.org/10.1145/3071178.3071258\">https://doi.org/10.1145/3071178.3071258</a>","ieee":"M. D. Wever, L. van Rooijen, and H. Hamann, “Active Coevolutionary Learning of Requirements Specifications from Examples,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)</i>, 2017, pp. 1327--1334.","ama":"Wever MD, van Rooijen L, Hamann H. Active Coevolutionary Learning of Requirements Specifications from Examples. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)</i>. ; 2017:1327--1334. doi:<a href=\"https://doi.org/10.1145/3071178.3071258\">10.1145/3071178.3071258</a>","bibtex":"@inproceedings{Wever_van Rooijen_Hamann_2017, title={Active Coevolutionary Learning of Requirements Specifications from Examples}, DOI={<a href=\"https://doi.org/10.1145/3071178.3071258\">10.1145/3071178.3071258</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}, author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2017}, pages={1327--1334} }","mla":"Wever, Marcel Dominik, et al. “Active Coevolutionary Learning of Requirements Specifications from Examples.” <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)</i>, 2017, pp. 1327--1334, doi:<a href=\"https://doi.org/10.1145/3071178.3071258\">10.1145/3071178.3071258</a>."},"file_date_updated":"2018-11-02T15:00:35Z","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Subproject B1","_id":"9"},{"name":"SFB 901 - Project Area B","_id":"3"}],"status":"public","has_accepted_license":"1","_id":"120","page":"1327--1334","user_id":"477","ddc":["000"],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)","abstract":[{"lang":"eng","text":"Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user’s needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson [2]. The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification."}],"date_created":"2017-10-17T12:41:15Z","file":[{"success":1,"content_type":"application/pdf","file_id":"5290","date_updated":"2018-11-02T15:00:35Z","relation":"main_file","file_size":1092021,"access_level":"closed","file_name":"p1327-wever.pdf","date_created":"2018-11-02T15:00:35Z","creator":"ups"}],"department":[{"_id":"66"}],"type":"conference","author":[{"full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176"},{"first_name":"Lorijn","last_name":"van Rooijen","full_name":"van Rooijen, Lorijn","id":"58843"},{"full_name":"Hamann, Heiko","first_name":"Heiko","last_name":"Hamann"}],"title":"Active Coevolutionary Learning of Requirements Specifications from Examples","year":"2017","date_updated":"2022-01-06T06:51:15Z","language":[{"iso":"eng"}],"doi":"10.1145/3071178.3071258"}]