{"has_accepted_license":"1","status":"public","date_created":"2017-10-17T12:41:27Z","author":[{"first_name":"Vitaly","full_name":"Melnikov, Vitaly","last_name":"Melnikov","id":"58747"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"}],"publication":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)","file_date_updated":"2018-03-21T12:32:44Z","file":[{"file_size":472159,"creator":"florida","file_id":"1533","content_type":"application/pdf","date_updated":"2018-03-21T12:32:44Z","success":1,"relation":"main_file","file_name":"184-chp_3A10.1007_2F978-3-319-46227-1_47.pdf","date_created":"2018-03-21T12:32:44Z","access_level":"closed"}],"ddc":["040"],"user_id":"15504","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."}],"type":"conference","year":"2016","citation":{"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.","ieee":"V. Melnikov and E. Hüllermeier, “Learning to Aggregate Using Uninorms,” in Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.","chicago":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.” In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 756–71. LNCS, 2016. https://doi.org/10.1007/978-3-319-46227-1_47.","ama":"Melnikov V, Hüllermeier E. Learning to Aggregate Using Uninorms. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016). LNCS. ; 2016:756-771. doi:10.1007/978-3-319-46227-1_47","apa":"Melnikov, V., & Hüllermeier, E. (2016). Learning to Aggregate Using Uninorms. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016) (pp. 756–771). https://doi.org/10.1007/978-3-319-46227-1_47","mla":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.” Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–71, doi:10.1007/978-3-319-46227-1_47.","bibtex":"@inproceedings{Melnikov_Hüllermeier_2016, series={LNCS}, title={Learning to Aggregate Using Uninorms}, DOI={10.1007/978-3-319-46227-1_47}, 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} }"},"page":"756-771","_id":"184","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Subprojekt B3","_id":"11"},{"_id":"3","name":"SFB 901 - Project Area B"}],"department":[{"_id":"355"}],"title":"Learning to Aggregate Using Uninorms","language":[{"iso":"eng"}],"series_title":"LNCS","doi":"10.1007/978-3-319-46227-1_47","date_updated":"2022-01-06T06:53:32Z"}