[{"author":[{"orcid":"0000-0002-9461-7372","first_name":"Stefan Helmut","last_name":"Heid","full_name":"Heid, Stefan Helmut","id":"39640"},{"first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","id":"33176"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"status":"public","title":"Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction","year":"2020","publication_status":"submitted","date_updated":"2022-01-06T06:53:15Z","_id":"17605","publisher":"episciences","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://arxiv.org/abs/2008.01377","open_access":"1"}],"user_id":"5786","citation":{"mla":"Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” <i>Journal of Data Mining and Digital Humanities</i>, episciences.","ama":"Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. <i>Journal of Data Mining and Digital Humanities</i>.","bibtex":"@article{Heid_Wever_Hüllermeier, title={Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction}, journal={Journal of Data Mining and Digital Humanities}, publisher={episciences}, author={Heid, Stefan Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke} }","apa":"Heid, S. H., Wever, M. D., &#38; Hüllermeier, E. (n.d.). Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. In <i>Journal of Data Mining and Digital Humanities</i>. episciences.","ieee":"S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction,” <i>Journal of Data Mining and Digital Humanities</i>. episciences.","chicago":"Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” <i>Journal of Data Mining and Digital Humanities</i>. episciences, n.d.","short":"S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital Humanities (n.d.)."},"publication":"Journal of Data Mining and Digital Humanities","project":[{"name":"InterGramm","_id":"39"}],"abstract":[{"text":"Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. \r\nWhile the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography.\r\nThese irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead  of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small.\r\nIn our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.","lang":"eng"}],"date_created":"2020-08-05T06:52:53Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"oa":"1","type":"preprint"},{"author":[{"id":"38209","first_name":"Alexander","last_name":"Tornede","full_name":"Tornede, Alexander"},{"id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"conference":{"location":"Online","name":"Workshop MetaLearn 2020 @ NeurIPS 2020"},"title":"Towards Meta-Algorithm Selection","status":"public","year":"2020","date_updated":"2022-01-06T06:54:26Z","_id":"20306","language":[{"iso":"eng"}],"user_id":"5786","citation":{"ama":"Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In: <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>. ; 2020.","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} }","mla":"Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020.","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards Meta-Algorithm Selection.” In <i>Workshop MetaLearn 2020 @ NeurIPS 2020</i>, 2020.","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.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,” presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020."},"publication":"Workshop MetaLearn 2020 @ NeurIPS 2020","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2020-11-06T09:42:27Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"conference"},{"citation":{"ieee":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach,” in <i>Learning and Intelligent Optimization. LION 2020.</i>, vol. 12096, Cham: Springer, 2020, pp. 216–232.","apa":"El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., &#38; Tierney, K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In <i>Learning and Intelligent Optimization. LION 2020.</i> (Vol. 12096, pp. 216–232). Cham: Springer. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">https://doi.org/10.1007/978-3-030-53552-0_22</a>","chicago":"El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” In <i>Learning and Intelligent Optimization. LION 2020.</i>, 12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">https://doi.org/10.1007/978-3-030-53552-0_22</a>.","short":"A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, K. Tierney, in: Learning and Intelligent Optimization. LION 2020., Springer, Cham, 2020, pp. 216–232.","mla":"El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.” <i>Learning and Intelligent Optimization. LION 2020.</i>, vol. 12096, Springer, 2020, pp. 216–32, doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>.","bibtex":"@inbook{El Mesaoudi-Paul_Weiß_Bengs_Hüllermeier_Tierney_2020, place={Cham}, series={Lecture Notes in Computer Science}, title={Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach}, volume={12096}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>}, booktitle={Learning and Intelligent Optimization. LION 2020.}, publisher={Springer}, author={El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2020}, pages={216–232}, collection={Lecture Notes in Computer Science} }","ama":"El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: <i>Learning and Intelligent Optimization. LION 2020.</i> Vol 12096. Lecture Notes in Computer Science. Cham: Springer; 2020:216-232. doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_22\">10.1007/978-3-030-53552-0_22</a>"},"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"place":"Cham","status":"public","page":"216 - 232","_id":"18014","publisher":"Springer","user_id":"76599","volume":12096,"publication":"Learning and Intelligent Optimization. LION 2020.","date_created":"2020-08-17T11:44:37Z","type":"book_chapter","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"title":"Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach","year":"2020","publication_identifier":{"isbn":["9783030535513","9783030535520"],"issn":["0302-9743","1611-3349"]},"author":[{"full_name":"El Mesaoudi-Paul, Adil","first_name":"Adil","last_name":"El Mesaoudi-Paul"},{"full_name":"Weiß, Dimitri","last_name":"Weiß","first_name":"Dimitri"},{"id":"76599","first_name":"Viktor","last_name":"Bengs","full_name":"Bengs, Viktor"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"},{"first_name":"Kevin","last_name":"Tierney","full_name":"Tierney, Kevin"}],"date_updated":"2022-01-06T06:53:25Z","publication_status":"published","intvolume":"     12096","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-53552-0_22"},{"_id":"18017","language":[{"iso":"eng"}],"user_id":"76599","year":"2020","title":"Online Preselection with Context Information under the Plackett-Luce  Model","status":"public","author":[{"last_name":"El Mesaoudi-Paul","first_name":"Adil","full_name":"El Mesaoudi-Paul, Adil"},{"id":"76599","last_name":"Bengs","first_name":"Viktor","full_name":"Bengs, Viktor"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"date_updated":"2022-01-06T06:53:25Z","publication_status":"draft","date_created":"2020-08-17T11:49:40Z","type":"preprint","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"arXiv:2002.04275","citation":{"ieee":"A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with Context Information under the Plackett-Luce  Model,” <i>arXiv:2002.04275</i>. .","apa":"El Mesaoudi-Paul, A., Bengs, V., &#38; Hüllermeier, E. (n.d.). Online Preselection with Context Information under the Plackett-Luce  Model. <i>ArXiv:2002.04275</i>.","mla":"El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information under the Plackett-Luce  Model.” <i>ArXiv:2002.04275</i>.","bibtex":"@article{El Mesaoudi-Paul_Bengs_Hüllermeier, title={Online Preselection with Context Information under the Plackett-Luce  Model}, journal={arXiv:2002.04275}, author={El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke} }","ama":"El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context Information under the Plackett-Luce  Model. <i>arXiv:200204275</i>.","short":"A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).","chicago":"El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection with Context Information under the Plackett-Luce  Model.” <i>ArXiv:2002.04275</i>, n.d."},"abstract":[{"text":"We consider an extension of the contextual multi-armed bandit problem, in\r\nwhich, instead of selecting a single alternative (arm), a learner is supposed\r\nto make a preselection in the form of a subset of alternatives. More\r\nspecifically, in each iteration, the learner is presented a set of arms and a\r\ncontext, both described in terms of feature vectors. The task of the learner is\r\nto preselect $k$ of these arms, among which a final choice is made in a second\r\nstep. In our setup, we assume that each arm has a latent (context-dependent)\r\nutility, and that feedback on a preselection is produced according to a\r\nPlackett-Luce model. We propose the CPPL algorithm, which is inspired by the\r\nwell-known UCB algorithm, and evaluate this algorithm on synthetic and real\r\ndata. In particular, we consider an online algorithm selection scenario, which\r\nserved as a main motivation of our problem setting. Here, an instance (which\r\ndefines the context) from a certain problem class (such as SAT) can be solved\r\nby different algorithms (the arms), but only $k$ of these algorithms can\r\nactually be run.","lang":"eng"}],"project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}]},{"date_updated":"2022-01-06T06:53:28Z","author":[{"id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede","first_name":"Alexander"},{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"full_name":"Werner, Stefan","last_name":"Werner","first_name":"Stefan"},{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"conference":{"end_date":"2020-11-20","start_date":"2020-11-18","name":"12th Asian Conference on Machine Learning","location":"Bangkok, Thailand"},"year":"2020","status":"public","title":"Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis","user_id":"5786","language":[{"iso":"eng"}],"_id":"18276","main_file_link":[{"url":"https://arxiv.org/pdf/2007.02816.pdf"}],"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"}],"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."}],"citation":{"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.","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.","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.","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.","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} }","mla":"Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis.” <i>ACML 2020</i>, 2020."},"publication":"ACML 2020","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"conference","date_created":"2020-08-25T12:09:28Z"},{"publication":"Journal of Automated Software Engineering","citation":{"apa":"Richter, C., Hüllermeier, E., Jakobs, M.-C., &#38; Wehrheim, H. (n.d.). Algorithm Selection for Software Validation Based on Graph Kernels. <i>Journal of Automated Software Engineering</i>.","ieee":"C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection for Software Validation Based on Graph Kernels,” <i>Journal of Automated Software Engineering</i>.","chicago":"Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Algorithm Selection for Software Validation Based on Graph Kernels.” <i>Journal of Automated Software Engineering</i>, n.d.","short":"C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated Software Engineering (n.d.).","mla":"Richter, Cedric, et al. “Algorithm Selection for Software Validation Based on Graph Kernels.” <i>Journal of Automated Software Engineering</i>, Springer.","ama":"Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software Validation Based on Graph Kernels. <i>Journal of Automated Software Engineering</i>.","bibtex":"@article{Richter_Hüllermeier_Jakobs_Wehrheim, title={Algorithm Selection for Software Validation Based on Graph Kernels}, journal={Journal of Automated Software Engineering}, publisher={Springer}, author={Richter, Cedric and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike} }"},"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"11","name":"SFB 901 - Subproject B3"},{"name":"SFB 901 - Subproject B4","_id":"12"}],"date_created":"2020-04-19T14:08:06Z","type":"journal_article","department":[{"_id":"7"},{"_id":"77"},{"_id":"355"}],"year":"2020","title":"Algorithm Selection for Software Validation Based on Graph Kernels","status":"public","author":[{"last_name":"Richter","first_name":"Cedric","full_name":"Richter, Cedric","id":"50003"},{"id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"},{"full_name":"Jakobs, Marie-Christine","first_name":"Marie-Christine","last_name":"Jakobs"},{"full_name":"Wehrheim, Heike","first_name":"Heike","last_name":"Wehrheim","id":"573"}],"date_updated":"2022-01-06T06:52:55Z","publication_status":"accepted","_id":"16725","publisher":"Springer","language":[{"iso":"eng"}],"user_id":"477"},{"language":[{"iso":"eng"}],"_id":"15629","publisher":"Springer","user_id":"5786","status":"public","year":"2020","title":"LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification","author":[{"full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"},{"first_name":"Alexander","last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"}],"conference":{"location":"Konstanz, Germany","start_date":"2020-04-24","name":"Symposium on Intelligent Data Analysis","end_date":"2020-04-27"},"publication_status":"accepted","date_updated":"2022-01-06T06:52:30Z","date_created":"2020-01-23T08:44:08Z","type":"conference","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"citation":{"short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.","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.","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.","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.","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.","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."},"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."}],"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"}]},{"date_created":"2019-11-18T14:19:19Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"},{"_id":"63"},{"_id":"238"}],"type":"journal_article","issue":"2","publication":"Evolutionary Computation","related_material":{"link":[{"url":"https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266","relation":"confirmation"}]},"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."}],"language":[{"iso":"eng"}],"doi":"10.1162/evco_a_00266","author":[{"full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","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":"Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets","year":"2020","intvolume":"        28","date_updated":"2022-01-06T06:52:15Z","publication_status":"published","citation":{"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} }","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>.","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>","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>."},"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"9","name":"SFB 901 - Subproject B1"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"publisher":"MIT Press Journals","_id":"15025","page":"165–193","volume":28,"user_id":"15415","status":"public"},{"publication":"arXiv:1901.10860","citation":{"ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Learning Choice Functions: Concepts and Architectures,” <i>arXiv:1901.10860</i>. 2019.","apa":"Pfannschmidt, K., Gupta, P., &#38; Hüllermeier, E. (2019). Learning Choice Functions: Concepts and Architectures. <i>ArXiv:1901.10860</i>.","short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019).","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice Functions: Concepts and Architectures.” <i>ArXiv:1901.10860</i>, 2019.","mla":"Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.” <i>ArXiv:1901.10860</i>, 2019.","bibtex":"@article{Pfannschmidt_Gupta_Hüllermeier_2019, title={Learning Choice Functions: Concepts and Architectures}, journal={arXiv:1901.10860}, author={Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2019} }","ama":"Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts and Architectures. <i>arXiv:190110860</i>. 2019."},"abstract":[{"lang":"eng","text":"We study the problem of learning choice functions, which play an important\r\nrole in various domains of application, most notably in the field of economics.\r\nFormally, a choice function is a mapping from sets to sets: Given a set of\r\nchoice alternatives as input, a choice function identifies a subset of most\r\npreferred elements. Learning choice functions from suitable training data comes\r\nwith a number of challenges. For example, the sets provided as input and the\r\nsubsets produced as output can be of any size. Moreover, since the order in\r\nwhich alternatives are presented is irrelevant, a choice function should be\r\nsymmetric. Perhaps most importantly, choice functions are naturally\r\ncontext-dependent, in the sense that the preference in favor of an alternative\r\nmay depend on what other options are available. We formalize the problem of\r\nlearning choice functions and present two general approaches based on two\r\nrepresentations of context-dependent utility functions. Both approaches are\r\ninstantiated by means of appropriate neural network architectures, and their\r\nperformance is demonstrated on suitable benchmark tasks."}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"date_created":"2020-09-17T10:53:38Z","type":"preprint","department":[{"_id":"7"},{"_id":"355"}],"status":"public","year":"2019","title":"Learning Choice Functions: Concepts and Architectures","author":[{"full_name":"Pfannschmidt, Karlson","last_name":"Pfannschmidt","first_name":"Karlson"},{"full_name":"Gupta, Pritha","first_name":"Pritha","last_name":"Gupta"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"date_updated":"2022-01-06T06:54:06Z","language":[{"iso":"eng"}],"_id":"19523","user_id":"13472"},{"publication_status":"published","date_updated":"2022-01-06T06:53:15Z","author":[{"full_name":"Merten, Marie-Luis","first_name":"Marie-Luis","last_name":"Merten"},{"full_name":"Seemann, Nina","first_name":"Nina","last_name":"Seemann"},{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"}],"year":"2019","title":"Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff","status":"public","user_id":"5786","language":[{"iso":"ger"}],"_id":"17565","page":"124-146","project":[{"name":"InterGramm","_id":"39"}],"citation":{"bibtex":"@article{Merten_Seemann_Wever_2019, title={Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff}, number={142}, journal={Niederdeutsches Jahrbuch}, author={Merten, Marie-Luis and Seemann, Nina and Wever, Marcel Dominik}, year={2019}, pages={124–146} }","ama":"Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. <i>Niederdeutsches Jahrbuch</i>. 2019;(142):124-146.","mla":"Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” <i>Niederdeutsches Jahrbuch</i>, no. 142, 2019, pp. 124–46.","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. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.” <i>Niederdeutsches Jahrbuch</i>, no. 142 (2019): 124–46.","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."},"issue":"142","publication":"Niederdeutsches Jahrbuch","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"type":"journal_article","date_created":"2020-08-03T13:55:04Z"},{"department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"type":"preprint","date_created":"2020-08-17T12:10:55Z","abstract":[{"text":"A common statistical task lies in showing asymptotic normality of certain\nstatistics. In many of these situations, classical textbook results on weak\nconvergence theory suffice for the problem at hand. However, there are quite\nsome scenarios where stronger results are needed in order to establish an\nasymptotic normal approximation uniformly over a family of probability\nmeasures. In this note we collect some results in this direction. We restrict\nourselves to weak convergence in $\\mathbb R^d$ with continuous limit measures.","lang":"eng"}],"citation":{"apa":"Bengs, V., &#38; Holzmann, H. (2019). Uniform approximation in classical weak convergence theory. <i>ArXiv:1903.09864</i>.","ieee":"V. Bengs and H. Holzmann, “Uniform approximation in classical weak convergence theory,” <i>arXiv:1903.09864</i>. 2019.","chicago":"Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” <i>ArXiv:1903.09864</i>, 2019.","short":"V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).","mla":"Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak Convergence Theory.” <i>ArXiv:1903.09864</i>, 2019.","ama":"Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory. <i>arXiv:190309864</i>. 2019.","bibtex":"@article{Bengs_Holzmann_2019, title={Uniform approximation in classical weak convergence theory}, journal={arXiv:1903.09864}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019} }"},"publication":"arXiv:1903.09864","user_id":"76599","_id":"18018","date_updated":"2022-01-06T06:53:25Z","author":[{"last_name":"Bengs","first_name":"Viktor","full_name":"Bengs, Viktor"},{"first_name":"Hajo","last_name":"Holzmann","full_name":"Holzmann, Hajo"}],"status":"public","title":"Uniform approximation in classical weak convergence theory","year":"2019"},{"author":[{"id":"33176","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever","full_name":"Wever, Marcel Dominik"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"full_name":"Hetzer, Alexander","first_name":"Alexander","last_name":"Hetzer","id":"38209"}],"conference":{"end_date":"2019-03-20","start_date":"2019-03-18","name":"European Conference on Data Analytics (ECDA)","location":"Bayreuth, Germany"},"year":"2019","title":"Towards Automated Machine Learning for Multi-Label Classification","status":"public","has_accepted_license":"1","date_updated":"2022-01-06T07:04:04Z","_id":"8868","language":[{"iso":"eng"}],"user_id":"49109","ddc":["000"],"citation":{"bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }","chicago":"Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer. “Towards Automated Machine Learning for Multi-Label Classification,” 2019.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.","ama":"Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning for Multi-Label Classification. In: ; 2019.","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. Presented at the European Conference on Data Analytics (ECDA), Bayreuth, Germany."},"file_date_updated":"2019-04-10T07:17:17Z","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"}],"date_created":"2019-04-10T07:17:55Z","file":[{"file_size":"74484","access_level":"closed","file_name":"Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf","date_updated":"2019-04-10T07:17:17Z","relation":"main_file","success":1,"content_type":"application/pdf","file_id":"8870","creator":"wever","date_created":"2019-04-10T07:17:17Z"}],"department":[{"_id":"355"}],"type":"conference_abstract"},{"issue":"2","publication":"New Mathematics and Natural Computation","citation":{"mla":"Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” <i>New Mathematics and Natural Computation</i>, vol. 15, no. 2, 2019, pp. 191–213.","bibtex":"@article{Tagne_Fotso_Fono_Hüllermeier_2019, title={Choice Functions Generated by Mallows and Plackett–Luce Relations}, volume={15}, number={2}, journal={New Mathematics and Natural Computation}, author={Tagne, V. K. and Fotso, S. and Fono, L. A.  and Hüllermeier, Eyke}, year={2019}, pages={191–213} }","ama":"Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows and Plackett–Luce Relations. <i>New Mathematics and Natural Computation</i>. 2019;15(2):191-213.","ieee":"V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated by Mallows and Plackett–Luce Relations,” <i>New Mathematics and Natural Computation</i>, vol. 15, no. 2, pp. 191–213, 2019.","apa":"Tagne, V. K., Fotso, S., Fono, L. A., &#38; Hüllermeier, E. (2019). Choice Functions Generated by Mallows and Plackett–Luce Relations. <i>New Mathematics and Natural Computation</i>, <i>15</i>(2), 191–213.","short":"V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural Computation 15 (2019) 191–213.","chicago":"Tagne, V. K., S. Fotso, L. A.  Fono, and Eyke Hüllermeier. “Choice Functions Generated by Mallows and Plackett–Luce Relations.” <i>New Mathematics and Natural Computation</i> 15, no. 2 (2019): 191–213."},"type":"journal_article","department":[{"_id":"34"},{"_id":"355"},{"_id":"7"}],"date_created":"2019-07-08T15:34:03Z","date_updated":"2022-01-06T06:50:45Z","intvolume":"        15","year":"2019","status":"public","title":"Choice Functions Generated by Mallows and Plackett–Luce Relations","author":[{"first_name":"V. K.","last_name":"Tagne","full_name":"Tagne, V. K."},{"last_name":"Fotso","first_name":"S.","full_name":"Fotso, S."},{"full_name":"Fono, L. A. ","first_name":"L. A. ","last_name":"Fono"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"}],"user_id":"315","volume":15,"page":"191-213","_id":"10578","language":[{"iso":"eng"}]},{"publication":"IEEE Computational Intelligence Magazine","citation":{"mla":"Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” <i>IEEE Computational Intelligence Magazine</i>, 2019, pp. 31–44, doi:<a href=\"https://doi.org/10.1109/mci.2018.2881642\">10.1109/mci.2018.2881642</a>.","bibtex":"@article{Couso_Borgelt_Hüllermeier_Kruse_2019, title={Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning}, DOI={<a href=\"https://doi.org/10.1109/mci.2018.2881642\">10.1109/mci.2018.2881642</a>}, journal={IEEE Computational Intelligence Magazine}, author={Couso, Ines and Borgelt, Christian and Hüllermeier, Eyke and Kruse, Rudolf}, year={2019}, pages={31–44} }","ama":"Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. <i>IEEE Computational Intelligence Magazine</i>. 2019:31-44. doi:<a href=\"https://doi.org/10.1109/mci.2018.2881642\">10.1109/mci.2018.2881642</a>","ieee":"I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning,” <i>IEEE Computational Intelligence Magazine</i>, pp. 31–44, 2019.","apa":"Couso, I., Borgelt, C., Hüllermeier, E., &#38; Kruse, R. (2019). Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. <i>IEEE Computational Intelligence Magazine</i>, 31–44. <a href=\"https://doi.org/10.1109/mci.2018.2881642\">https://doi.org/10.1109/mci.2018.2881642</a>","chicago":"Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning.” <i>IEEE Computational Intelligence Magazine</i>, 2019, 31–44. <a href=\"https://doi.org/10.1109/mci.2018.2881642\">https://doi.org/10.1109/mci.2018.2881642</a>.","short":"I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence Magazine (2019) 31–44."},"type":"journal_article","department":[{"_id":"34"},{"_id":"355"}],"date_created":"2019-11-15T10:11:37Z","date_updated":"2022-01-06T06:52:13Z","publication_status":"published","status":"public","year":"2019","title":"Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning","publication_identifier":{"issn":["1556-603X","1556-6048"]},"author":[{"last_name":"Couso","first_name":"Ines","full_name":"Couso, Ines"},{"full_name":"Borgelt, Christian","last_name":"Borgelt","first_name":"Christian"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"},{"last_name":"Kruse","first_name":"Rudolf","full_name":"Kruse, Rudolf"}],"doi":"10.1109/mci.2018.2881642","user_id":"315","page":"31-44","_id":"15001","language":[{"iso":"eng"}]},{"abstract":[{"text":"Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.","lang":"eng"}],"issue":"2","publication":"Data Mining and Knowledge Discovery","type":"journal_article","department":[{"_id":"34"},{"_id":"355"}],"file":[{"file_id":"16155","content_type":"application/pdf","relation":"main_file","date_updated":"2020-02-28T12:45:26Z","file_name":"multi-target-prediction.pdf","file_size":837808,"access_level":"open_access","date_created":"2020-02-28T12:43:39Z","creator":"lettmann"}],"date_created":"2019-11-15T10:16:34Z","date_updated":"2022-01-06T06:52:14Z","intvolume":"        33","title":"Multi-target prediction: a unifying view on problems and methods","year":"2019","author":[{"first_name":"Willem","last_name":"Waegeman","full_name":"Waegeman, Willem"},{"full_name":"Dembczynski, Krzysztof","first_name":"Krzysztof","last_name":"Dembczynski"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"publication_identifier":{"issn":["1573-756X"]},"doi":"10.1007/s10618-018-0595-5","language":[{"iso":"eng"}],"file_date_updated":"2020-02-28T12:45:26Z","citation":{"mla":"Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems and Methods.” <i>Data Mining and Knowledge Discovery</i>, vol. 33, no. 2, 2019, pp. 293–324, doi:<a href=\"https://doi.org/10.1007/s10618-018-0595-5\">10.1007/s10618-018-0595-5</a>.","bibtex":"@article{Waegeman_Dembczynski_Hüllermeier_2019, title={Multi-target prediction: a unifying view on problems and methods}, volume={33}, DOI={<a href=\"https://doi.org/10.1007/s10618-018-0595-5\">10.1007/s10618-018-0595-5</a>}, number={2}, journal={Data Mining and Knowledge Discovery}, author={Waegeman, Willem and Dembczynski, Krzysztof and Hüllermeier, Eyke}, year={2019}, pages={293–324} }","ama":"Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying view on problems and methods. <i>Data Mining and Knowledge Discovery</i>. 2019;33(2):293-324. doi:<a href=\"https://doi.org/10.1007/s10618-018-0595-5\">10.1007/s10618-018-0595-5</a>","ieee":"W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction: a unifying view on problems and methods,” <i>Data Mining and Knowledge Discovery</i>, vol. 33, no. 2, pp. 293–324, 2019.","apa":"Waegeman, W., Dembczynski, K., &#38; Hüllermeier, E. (2019). Multi-target prediction: a unifying view on problems and methods. <i>Data Mining and Knowledge Discovery</i>, <i>33</i>(2), 293–324. <a href=\"https://doi.org/10.1007/s10618-018-0595-5\">https://doi.org/10.1007/s10618-018-0595-5</a>","chicago":"Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target Prediction: A Unifying View on Problems and Methods.” <i>Data Mining and Knowledge Discovery</i> 33, no. 2 (2019): 293–324. <a href=\"https://doi.org/10.1007/s10618-018-0595-5\">https://doi.org/10.1007/s10618-018-0595-5</a>.","short":"W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324."},"oa":"1","has_accepted_license":"1","status":"public","ddc":["000"],"user_id":"315","volume":33,"page":"293-324","_id":"15002"},{"citation":{"ieee":"T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. Waegeman, “Set-Valued Prediction in Multi-Class Classification,” in <i>Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019</i>, 2019.","apa":"Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., &#38; Waegeman, W. (2019). Set-Valued Prediction in Multi-Class Classification. In <i>Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019</i>.","short":"T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. 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