{"_id":"184","title":"Learning to Aggregate Using Uninorms","language":[{"iso":"eng"}],"type":"conference","date_updated":"2022-01-06T06:53:32Z","page":"756-771","author":[{"last_name":"Melnikov","full_name":"Melnikov, Vitaly","id":"58747","first_name":"Vitaly"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129","first_name":"Eyke"}],"user_id":"15504","year":"2016","series_title":"LNCS","date_created":"2017-10-17T12:41:27Z","status":"public","file_date_updated":"2018-03-21T12:32:44Z","abstract":[{"lang":"eng","text":"In this paper, we propose a framework for a class of learning problems that we refer to as “learning to aggregate”. Roughly, learning-to-aggregate problems are supervised machine learning problems, in which instances are represented in the form of a composition of a (variable) number on constituents; such compositions are associated with an evaluation, score, or label, which is the target of the prediction task, and which can presumably be modeled in the form of a suitable aggregation of the properties of its constituents. Our learning-to-aggregate framework establishes a close connection between machine learning and a branch of mathematics devoted to the systematic study of aggregation functions. We specifically focus on a class of functions called uninorms, which combine conjunctive and disjunctive modes of aggregation. Experimental results for a corresponding model are presented for a review data set, for which the aggregation problem consists of combining different reviewer opinions about a paper into an overall decision of acceptance or rejection."}],"doi":"10.1007/978-3-319-46227-1_47","has_accepted_license":"1","file":[{"creator":"florida","date_created":"2018-03-21T12:32:44Z","date_updated":"2018-03-21T12:32:44Z","file_name":"184-chp_3A10.1007_2F978-3-319-46227-1_47.pdf","content_type":"application/pdf","relation":"main_file","access_level":"closed","file_size":472159,"success":1,"file_id":"1533"}],"ddc":["040"],"project":[{"name":"SFB 901","_id":"1"},{"_id":"11","name":"SFB 901 - Subprojekt B3"},{"_id":"3","name":"SFB 901 - Project Area B"}],"citation":{"ama":"Melnikov V, Hüllermeier E. Learning to Aggregate Using Uninorms. In: <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i>. LNCS. ; 2016:756-771. doi:<a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">10.1007/978-3-319-46227-1_47</a>","ieee":"V. Melnikov and E. Hüllermeier, “Learning to Aggregate Using Uninorms,” in <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i>, 2016, pp. 756–771.","short":"V. Melnikov, E. Hüllermeier, in: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.","apa":"Melnikov, V., &#38; Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i> (pp. 756–771). <a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">https://doi.org/10.1007/978-3-319-46227-1_47</a>","chicago":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.” In <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i>, 756–71. LNCS, 2016. <a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">https://doi.org/10.1007/978-3-319-46227-1_47</a>.","bibtex":"@inproceedings{Melnikov_Hüllermeier_2016, series={LNCS}, title={Learning to Aggregate Using Uninorms}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">10.1007/978-3-319-46227-1_47</a>}, booktitle={Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, year={2016}, pages={756–771}, collection={LNCS} }","mla":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.” <i>Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)</i>, 2016, pp. 756–71, doi:<a href=\"https://doi.org/10.1007/978-3-319-46227-1_47\">10.1007/978-3-319-46227-1_47</a>."},"publication":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)","department":[{"_id":"355"}]}