[{"language":[{"iso":"eng"}],"file_date_updated":"2019-09-10T08:20:44Z","ddc":["006"],"department":[{"_id":"355"}],"user_id":"33176","_id":"10232","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"status":"public","file":[{"file_size":388191,"file_name":"Automating_MultiLabel_Classification_Extending_ML-Plan.pdf","access_level":"open_access","file_id":"13177","date_updated":"2019-09-10T08:20:44Z","date_created":"2019-09-10T08:19:01Z","creator":"wever","relation":"main_file","content_type":"application/pdf"}],"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"}],"type":"conference","conference":{"end_date":"2019-06-15","location":"Long Beach, CA, USA","name":"6th ICML Workshop on Automated Machine Learning (AutoML 2019)","start_date":"2019-06-09"},"title":"Automating Multi-Label Classification Extending ML-Plan","date_created":"2019-06-11T21:33:06Z","author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","full_name":"Wever, Marcel Dominik","id":"33176","first_name":"Marcel Dominik"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"last_name":"Tornede","id":"38209","full_name":"Tornede, Alexander","first_name":"Alexander"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"oa":"1","date_updated":"2022-01-06T06:50:33Z","citation":{"ama":"Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 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.","chicago":"Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 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} }","mla":"Wever, Marcel Dominik, et al. <i>Automating Multi-Label Classification Extending ML-Plan</i>. 2019.","short":"M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019."},"year":"2019","has_accepted_license":"1"},{"citation":{"apa":"Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., &#38; Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. <i>IEEE Transactions on Cognitive and Developmental Systems</i>. <a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">https://doi.org/10.1109/TCDS.2019.2892991</a>","short":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).","bibtex":"@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019, title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches}, DOI={<a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">10.1109/TCDS.2019.2892991</a>}, journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing, Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna and Hüllermeier, Eyke}, year={2019} }","mla":"Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” <i>IEEE Transactions on Cognitive and Developmental Systems</i>, 2019, doi:<a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">10.1109/TCDS.2019.2892991</a>.","ama":"Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. <i>IEEE Transactions on Cognitive and Developmental Systems</i>. Published online 2019. doi:<a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">10.1109/TCDS.2019.2892991</a>","chicago":"Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” <i>IEEE Transactions on Cognitive and Developmental Systems</i>, 2019. <a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">https://doi.org/10.1109/TCDS.2019.2892991</a>.","ieee":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,” <i>IEEE Transactions on Cognitive and Developmental Systems</i>, 2019, doi: <a href=\"https://doi.org/10.1109/TCDS.2019.2892991\">10.1109/TCDS.2019.2892991</a>."},"year":"2019","date_created":"2020-11-02T13:25:49Z","author":[{"last_name":"Rohlfing","id":"50352","full_name":"Rohlfing, Katharina","first_name":"Katharina"},{"last_name":"Leonardi","full_name":"Leonardi, Giuseppe","first_name":"Giuseppe"},{"full_name":"Nomikou, Iris","last_name":"Nomikou","first_name":"Iris"},{"first_name":"Joanna","full_name":"Rączaszek-Leonardi, Joanna","last_name":"Rączaszek-Leonardi"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"date_updated":"2023-02-01T12:39:19Z","doi":"10.1109/TCDS.2019.2892991","title":"Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches","type":"journal_article","publication":"IEEE Transactions on Cognitive and Developmental Systems","status":"public","user_id":"14931","department":[{"_id":"749"},{"_id":"355"}],"_id":"20243","language":[{"iso":"eng"}]},{"date_updated":"2022-01-06T06:56:35Z","oa":"1","author":[{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"first_name":"Amin","full_name":"Faez, Amin","last_name":"Faez"}],"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"},"doi":"10.1109/SCC.2018.00039","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/8456425","open_access":"1"}],"has_accepted_license":"1","publication_status":"published","place":"San Francisco, CA, USA","citation":{"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>.","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>","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>.","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} }","short":"F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 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>"},"_id":"2479","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"department":[{"_id":"355"}],"user_id":"49109","file_date_updated":"2018-11-06T15:08:39Z","type":"conference","status":"public","publisher":"IEEE","date_created":"2018-04-24T08:34:52Z","title":"(WIP) Towards the Automated Composition of Machine Learning Services","year":"2018","ddc":["000"],"language":[{"iso":"eng"}],"publication":"SCC","file":[{"file_size":237890,"access_level":"closed","file_id":"5382","file_name":"08456425.pdf","date_updated":"2018-11-06T15:08:39Z","creator":"wever","date_created":"2018-11-06T15:08:39Z","relation":"main_file","content_type":"application/pdf"}]},{"conference":{"location":"Delft, Netherlands","end_date":"2018-06-29","start_date":"2018-06-24","name":"28th International Conference on Automated Planning and Scheduling"},"main_file_link":[{"open_access":"1","url":"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf"}],"date_updated":"2022-01-06T06:58:08Z","oa":"1","author":[{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Lettmann","orcid":"0000-0001-5859-2457","id":"315","full_name":"Lettmann, Theodor","first_name":"Theodor"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"}],"page":"31-39","citation":{"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} }","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.","short":"F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.","apa":"Mohr, F., Lettmann, T., Hüllermeier, E., &#38; Wever, M. D. (2018). Programmatic Task Network Planning. In <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i> (pp. 31–39). Delft, Netherlands: AAAI.","ieee":"F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i>, Delft, Netherlands, 2018, pp. 31–39.","chicago":"Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever. “Programmatic Task Network Planning.” In <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i>, 31–39. AAAI, 2018.","ama":"Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i>. AAAI; 2018:31-39."},"has_accepted_license":"1","file_date_updated":"2018-11-06T15:18:26Z","_id":"2857","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"department":[{"_id":"355"}],"user_id":"315","status":"public","type":"conference","title":"Programmatic Task Network Planning","publisher":"AAAI","date_created":"2018-05-24T09:00:20Z","year":"2018","ddc":["000"],"language":[{"iso":"eng"}],"file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":349958,"file_id":"5384","access_level":"closed","file_name":"Mohr18ProgrammaticPlanning.pdf","date_updated":"2018-11-06T15:18:26Z","creator":"wever","date_created":"2018-11-06T15:18:26Z"}],"publication":"Proceedings of the 1st ICAPS Workshop on Hierarchical Planning"},{"file":[{"content_type":"application/pdf","relation":"main_file","success":1,"creator":"wever","date_created":"2018-11-06T15:15:38Z","date_updated":"2018-11-06T15:15:38Z","file_id":"5383","access_level":"closed","file_name":"08456422.pdf","file_size":356132}],"publication":"SCC","ddc":["000"],"language":[{"iso":"eng"}],"year":"2018","title":"On-The-Fly Service Construction with Prototypes","publisher":"IEEE Computer Society","date_created":"2018-04-23T11:40:20Z","status":"public","type":"conference","file_date_updated":"2018-11-06T15:15:38Z","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"_id":"2471","user_id":"49109","department":[{"_id":"355"}],"place":"San Francisco, CA, USA","citation":{"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.","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>.","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>","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>","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} }","short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018."},"has_accepted_license":"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"},"oa":"1","date_updated":"2022-01-06T06:56:32Z","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"full_name":"Wever, Marcel Dominik","id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}]},{"status":"public","file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":1482882,"access_level":"closed","file_id":"5305","file_name":"OnTheEffectivenessOfHeuristics.pdf","date_updated":"2018-11-02T15:30:57Z","creator":"ups","date_created":"2018-11-02T15:30:57Z"}],"abstract":[{"lang":"eng","text":"In machine learning, so-called nested dichotomies are utilized as a reduction technique, i.e., to decompose a multi-class classification problem into a set of binary problems, which are solved using a simple binary classifier as a base learner. The performance of the (multi-class) classifier thus produced strongly depends on the structure of the decomposition. In this paper, we conduct an empirical study, in which we compare existing heuristics for selecting a suitable structure in the form of a nested dichotomy. Moreover, we propose two additional heuristics as natural completions. One of them is the Best-of-K heuristic, which picks the (presumably) best among K randomly generated nested dichotomies. Surprisingly, and in spite of its simplicity, it turns out to outperform the state of the art."}],"publication":"Machine Learning","type":"journal_article","file_date_updated":"2018-11-02T15:30:57Z","language":[{"iso":"eng"}],"ddc":["000"],"department":[{"_id":"355"}],"user_id":"15504","_id":"3402","project":[{"name":"SFB 901 - Subproject B3","_id":"11"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901","_id":"1"}],"citation":{"ieee":"V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” <i>Machine Learning</i>, 2018.","chicago":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” <i>Machine Learning</i>, 2018. <a href=\"https://doi.org/10.1007/s10994-018-5733-1\">https://doi.org/10.1007/s10994-018-5733-1</a>.","ama":"Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. <i>Machine Learning</i>. 2018. doi:<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>","apa":"Melnikov, V., &#38; Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. <i>Machine Learning</i>. <a href=\"https://doi.org/10.1007/s10994-018-5733-1\">https://doi.org/10.1007/s10994-018-5733-1</a>","mla":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” <i>Machine Learning</i>, 2018, doi:<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>.","bibtex":"@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis}, DOI={<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>}, journal={Machine Learning}, author={Melnikov, Vitalik and Hüllermeier, Eyke}, year={2018} }","short":"V. Melnikov, E. Hüllermeier, Machine Learning (2018)."},"year":"2018","publication_identifier":{"issn":["1573-0565"]},"has_accepted_license":"1","doi":"10.1007/s10994-018-5733-1","title":"On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis","author":[{"first_name":"Vitalik","last_name":"Melnikov","full_name":"Melnikov, Vitalik"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"}],"date_created":"2018-06-29T07:44:26Z","date_updated":"2022-01-06T06:59:14Z"},{"type":"journal_article","status":"public","project":[{"name":"SFB 901","_id":"1"},{"_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"}],"_id":"3510","user_id":"5786","department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"article_type":"original","file_date_updated":"2018-11-02T15:32:16Z","publication_status":"epub_ahead","has_accepted_license":"1","publication_identifier":{"issn":["0885-6125"],"eissn":["1573-0565"]},"citation":{"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>","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>.","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} }","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>"},"page":"1495-1515","date_updated":"2022-01-06T06:59:21Z","oa":"1","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"}],"main_file_link":[{"url":"https://rdcu.be/3Nc2","open_access":"1"}],"doi":"10.1007/s10994-018-5735-z","conference":{"name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","start_date":"2018-09-10","end_date":"2018-09-14","location":"Dublin, Ireland"},"publication":"Machine Learning","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":[{"date_updated":"2018-11-02T15:32:16Z","date_created":"2018-11-02T15:32:16Z","creator":"ups","file_size":1070937,"file_name":"ML-PlanAutomatedMachineLearnin.pdf","access_level":"closed","file_id":"5306","content_type":"application/pdf","success":1,"relation":"main_file"}],"ddc":["000"],"keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"language":[{"iso":"eng"}],"year":"2018","publisher":"Springer","date_created":"2018-07-08T14:06:14Z","title":"ML-Plan: Automated Machine Learning via Hierarchical Planning"},{"publication_status":"accepted","has_accepted_license":"1","citation":{"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} }","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>.","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>","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>","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>.","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."},"place":"‘s-Hertogenbosch, the Netherlands","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"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"oa":"1","date_updated":"2022-01-06T06:59:25Z","main_file_link":[{"open_access":"1","url":"https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19"}],"conference":{"start_date":"2018-10-24","name":"Symposium on Intelligent Data Analysis","location":"‘s-Hertogenbosch, the Netherlands","end_date":"2018-10-26"},"doi":"10.1007/978-3-030-01768-2_19","type":"conference","status":"public","user_id":"49109","department":[{"_id":"355"}],"project":[{"_id":"1","name":"SFB 901"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"SFB 901 - Project Area B","_id":"3"}],"_id":"3552","file_date_updated":"2018-11-06T15:23:02Z","quality_controlled":"1","year":"2018","date_created":"2018-07-13T15:29:15Z","title":"Reduction Stumps for Multi-Class Classification","publication":"Proceedings of the Symposium on Intelligent Data Analysis","file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":1348768,"file_name":"Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf","file_id":"5385","access_level":"closed","date_updated":"2018-11-06T15:23:02Z","date_created":"2018-11-06T15:23:02Z","creator":"wever"}],"language":[{"iso":"eng"}],"ddc":["000"]},{"year":"2018","quality_controlled":"1","title":"ML-Plan for Unlimited-Length Machine Learning Pipelines","date_created":"2018-08-09T06:14:54Z","abstract":[{"lang":"eng","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."}],"file":[{"relation":"main_file","content_type":"application/pdf","file_name":"38.pdf","file_id":"3853","access_level":"open_access","file_size":297811,"date_created":"2018-08-09T06:14:43Z","creator":"wever","date_updated":"2018-08-09T06:14:43Z"}],"publication":"ICML 2018 AutoML Workshop","keyword":["automated machine learning","complex pipelines","hierarchical planning"],"ddc":["006"],"language":[{"iso":"eng"}],"citation":{"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} }","apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In <i>ICML 2018 AutoML Workshop</i>. Stockholm, Sweden.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in <i>ICML 2018 AutoML Workshop</i>, Stockholm, Sweden, 2018.","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.","ama":"Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: <i>ICML 2018 AutoML Workshop</i>. ; 2018."},"has_accepted_license":"1","conference":{"name":"ICML 2018 AutoML Workshop","start_date":"2018-07-10","end_date":"2018-07-15","location":"Stockholm, Sweden"},"main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"oa":"1","date_updated":"2022-01-06T06:59:46Z","author":[{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","id":"33176"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"urn":"38527","status":"public","type":"conference","file_date_updated":"2018-08-09T06:14:43Z","_id":"3852","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"department":[{"_id":"355"}],"user_id":"49109"},{"year":"2018","publisher":"ACM","date_created":"2018-03-31T13:51:23Z","title":"Ensembles of Evolved Nested Dichotomies for Classification","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_updated":"2018-11-02T14:33:54Z","creator":"ups","date_created":"2018-11-02T14:33:54Z","file_size":875404,"access_level":"closed","file_id":"5275","file_name":"p561-wever.pdf","content_type":"application/pdf","success":1,"relation":"main_file"}],"keyword":["Classification","Hierarchical Decomposition","Indirect Encoding"],"ddc":["000"],"language":[{"iso":"eng"}],"has_accepted_license":"1","publication_status":"published","place":"Kyoto, Japan","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.","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} }","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>"},"oa":"1","date_updated":"2022-01-06T06:54:45Z","author":[{"id":"33176","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"conference":{"end_date":"2018-07-19","location":"Kyoto, Japan","name":"GECCO 2018","start_date":"2018-07-15"},"doi":"10.1145/3205455.3205562","main_file_link":[{"url":"https://dl.acm.org/citation.cfm?doid=3205455.3205562","open_access":"1"}],"type":"conference","status":"public","_id":"2109","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"}],"department":[{"_id":"355"}],"user_id":"33176","file_date_updated":"2018-11-02T14:33:54Z"},{"user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"_id":"17713","language":[{"iso":"eng"}],"type":"preprint","status":"public","date_created":"2020-08-07T11:38:10Z","author":[{"first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176","full_name":"Wever, Marcel Dominik"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"}],"oa":"1","date_updated":"2022-01-06T06:53:17Z","publisher":"Arxiv","main_file_link":[{"url":"https://arxiv.org/pdf/1811.04060.pdf","open_access":"1"}],"title":"Automated Multi-Label Classification based on ML-Plan","citation":{"apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). <i>Automated Multi-Label Classification based on ML-Plan</i>. Arxiv.","mla":"Wever, Marcel Dominik, et al. <i>Automated Multi-Label Classification Based on ML-Plan</i>. Arxiv, 2018.","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} }","ama":"Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. Published online 2018.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018."},"year":"2018"},{"language":[{"iso":"eng"}],"project":[{"_id":"1","name":"SFB 901"},{"_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"}],"_id":"17714","user_id":"5786","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"status":"public","type":"preprint","title":"Automated machine learning service composition","main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/1809.00486.pdf"}],"date_updated":"2022-01-06T06:53:17Z","oa":"1","author":[{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"},{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129","last_name":"Hüllermeier"}],"date_created":"2020-08-07T11:40:13Z","year":"2018","citation":{"chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine Learning Service Composition,” 2018.","ieee":"F. 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(2018). <i>Automated machine learning service composition</i>."}},{"publication":"Discovery Science","file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":458972,"file_name":"Schäfer-Hüllermeier2018_Chapter_Preference-BasedReinforcementL.pdf","file_id":"6623","access_level":"closed","date_updated":"2019-01-11T11:03:50Z","date_created":"2019-01-11T11:03:50Z","creator":"ups"}],"language":[{"iso":"eng"}],"ddc":["000"],"year":"2018","date_created":"2018-12-20T15:52:03Z","publisher":"Springer International Publishing","title":"Preference-Based Reinforcement Learning Using Dyad Ranking","type":"book_chapter","status":"public","department":[{"_id":"355"}],"user_id":"49109","_id":"6423","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"file_date_updated":"2019-01-11T11:03:50Z","has_accepted_license":"1","publication_identifier":{"isbn":["9783030017705","9783030017712"],"issn":["0302-9743","1611-3349"]},"publication_status":"published","page":"161-175","citation":{"mla":"Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” <i>Discovery Science</i>, Springer International Publishing, 2018, pp. 161–75, doi:<a href=\"https://doi.org/10.1007/978-3-030-01771-2_11\">10.1007/978-3-030-01771-2_11</a>.","short":"D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International Publishing, Cham, 2018, pp. 161–175.","bibtex":"@inbook{Schäfer_Hüllermeier_2018, place={Cham}, title={Preference-Based Reinforcement Learning Using Dyad Ranking}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-01771-2_11\">10.1007/978-3-030-01771-2_11</a>}, booktitle={Discovery Science}, publisher={Springer International Publishing}, author={Schäfer, Dirk and Hüllermeier, Eyke}, year={2018}, pages={161–175} }","apa":"Schäfer, D., &#38; Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In <i>Discovery Science</i> (pp. 161–175). Cham: Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-01771-2_11\">https://doi.org/10.1007/978-3-030-01771-2_11</a>","ama":"Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad Ranking. In: <i>Discovery Science</i>. Cham: Springer International Publishing; 2018:161-175. doi:<a href=\"https://doi.org/10.1007/978-3-030-01771-2_11\">10.1007/978-3-030-01771-2_11</a>","ieee":"D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using Dyad Ranking,” in <i>Discovery Science</i>, Cham: Springer International Publishing, 2018, pp. 161–175.","chicago":"Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning Using Dyad Ranking.” In <i>Discovery Science</i>, 161–75. 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