[{"oa":"1","user_id":"315","file":[{"date_updated":"2020-02-28T12:45:26Z","relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_id":"16155","date_created":"2020-02-28T12:43:39Z","creator":"lettmann","file_size":837808,"file_name":"multi-target-prediction.pdf"}],"title":"Multi-target prediction: a unifying view on problems and methods","doi":"10.1007/s10618-018-0595-5","has_accepted_license":"1","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"}],"volume":33,"page":"293-324","issue":"2","publication":"Data Mining and Knowledge Discovery","ddc":["000"],"type":"journal_article","citation":{"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.","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>.","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>","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>","short":"W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery 33 (2019) 293–324.","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} }","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>."},"department":[{"_id":"34"},{"_id":"355"}],"author":[{"last_name":"Waegeman","first_name":"Willem","full_name":"Waegeman, Willem"},{"first_name":"Krzysztof","full_name":"Dembczynski, Krzysztof","last_name":"Dembczynski"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier","id":"48129"}],"intvolume":"        33","_id":"15002","file_date_updated":"2020-02-28T12:45:26Z","date_updated":"2022-01-06T06:52:14Z","date_created":"2019-11-15T10:16:34Z","status":"public","publication_identifier":{"issn":["1573-756X"]},"year":"2019","language":[{"iso":"eng"}]}]