{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T20:34:47Z","timestamp":1754598887262,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030659646"},{"type":"electronic","value":"9783030659653"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-65965-3_23","type":"book-chapter","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T19:48:52Z","timestamp":1612208932000},"page":"353-362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Towards Better Evaluation of Multi-target Regression Models"],"prefix":"10.1007","author":[{"given":"Evgeniya","family":"Korneva","sequence":"first","affiliation":[]},{"given":"Hendrik","family":"Blockeel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,2]]},"reference":[{"key":"23_CR1","unstructured":"Aho, T., \u017denko, B., D\u017eeroski, S., Elomaa, T.: Multi-target regression with rule ensembles. J. Mach. Learn. Res. 13(Aug), 2367\u20132407 (2012)"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Benard, A.P., vanElteren, P.: A generalization of the method of m rankings. Indagationes Mathematicae 1(5), 358\u2013369 (1953)","DOI":"10.1016\/S1385-7258(53)50045-9"},{"issue":"11","key":"23_CR3","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1007\/s10994-018-5744-y","volume":"107","author":"M Breskvar","year":"2018","unstructured":"Breskvar, M., Kocev, D., D\u017eeroski, S.: Ensembles for multi-target regression with random output selections. Mach. Learn. 107(11), 1673\u20131709 (2018). https:\/\/doi.org\/10.1007\/s10994-018-5744-y","journal-title":"Mach. Learn."},{"key":"23_CR4","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(Jan), 1\u201330 (2006)"},{"key":"23_CR5","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"issue":"1","key":"23_CR6","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1008323212047","volume":"13","author":"S D\u017eeroski","year":"2000","unstructured":"D\u017eeroski, S., Dem\u0161ar, D., Grbovi\u0107, J.: Predicting chemical parameters of river water quality from bioindicator data. Appl. Intell. 13(1), 7\u201317 (2000)","journal-title":"Appl. Intell."},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Goovaerts, P.: Geostatistics for natural resources evaluation. Oxford University Press on Demand (1997)","DOI":"10.1093\/oso\/9780195115383.001.0001"},{"key":"23_CR8","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.engappai.2015.06.022","volume":"45","author":"E Hadavandi","year":"2015","unstructured":"Hadavandi, E., Shahrabi, J., Shamshirband, S.: A novel boosted-neural network ensemble for modeling multi-target regression problems. Eng. Appl. Artif. Intell. 45, 204\u2013219 (2015)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"23_CR9","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.knosys.2008.03.005","volume":"21","author":"EV Hatzikos","year":"2008","unstructured":"Hatzikos, E.V., Tsoumakas, G., Tzanis, G., Bassiliades, N., Vlahavas, I.: An empirical study on sea water quality prediction. Knowl.-Based Syst. 21(6), 471\u2013478 (2008)","journal-title":"Knowl.-Based Syst."},{"issue":"2\u20133","key":"23_CR10","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1023\/A:1007365207130","volume":"26","author":"A Karali\u010d","year":"1997","unstructured":"Karali\u010d, A., Bratko, I.: First order regression. Mach. Learn. 26(2\u20133), 147\u2013176 (1997)","journal-title":"Mach. Learn."},{"issue":"3","key":"23_CR11","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/j.patcog.2012.09.023","volume":"46","author":"D Kocev","year":"2013","unstructured":"Kocev, D., Vens, C., Struyf, J., D\u017eeroski, S.: Tree ensembles for predicting structured outputs. Pattern Recogn. 46(3), 817\u2013833 (2013)","journal-title":"Pattern Recogn."},{"key":"23_CR12","unstructured":"Mastelini, S.M., Santana, E.J., da Costa, V.G.T., Barbon, S.: Benchmarking multi-target regression methods. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pp. 396\u2013401. IEEE (2018)"},{"key":"23_CR13","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.ins.2017.06.017","volume":"415","author":"G Melki","year":"2017","unstructured":"Melki, G., Cano, A., Kecman, V., Ventura, S.: Multi-target support vector regression via correlation regressor chains. Inf. Sci. 415, 53\u201369 (2017)","journal-title":"Inf. Sci."},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"O\u2019Brien, P.C.: Procedures for comparing samples with multiple endpoints. Biometrics 1079\u20131087 (1984)","DOI":"10.2307\/2531158"},{"key":"23_CR15","doi-asserted-by":"publisher","unstructured":"Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., Vlahavas, I.: Multi-target regression via input space expansion: treating targets as inputs. Mach. Learn. 104(1), 55\u201398 (2016). https:\/\/doi.org\/10.1007\/s10994-016-5546-z","DOI":"10.1007\/s10994-016-5546-z"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build. 49, 560\u2013567 (2012)","DOI":"10.1016\/j.enbuild.2012.03.003"},{"key":"23_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/978-3-662-44845-8_15","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"G Tsoumakas","year":"2014","unstructured":"Tsoumakas, G., Spyromitros-Xioufis, E., Vrekou, A., Vlahavas, I.: Multi-target regression via random linear target combinations. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8726, pp. 225\u2013240. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44845-8_15"},{"key":"23_CR18","unstructured":"Tuia, D., Verrelst, J., Alonso, L., P\u00e9rez-Cruz, F., Camps-Valls, G.: Multioutput support vector regression for remote sensing biophysical parameter estimation. IEEE Geosci. Remote Sens. Lett. 8(4), 804\u2013808 (2011)"},{"issue":"9","key":"23_CR19","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1016\/j.patrec.2013.01.015","volume":"34","author":"S Xu","year":"2013","unstructured":"Xu, S., An, X., Qiao, X., Zhu, L., Li, L.: Multi-output least-squares support vector regression machines. Pattern Recogn. Lett. 34(9), 1078\u20131084 (2013)","journal-title":"Pattern Recogn. Lett."},{"issue":"6","key":"23_CR20","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.cemconcomp.2007.02.001","volume":"29","author":"IC Yeh","year":"2007","unstructured":"Yeh, I.C.: Modeling slump flow of concrete using second-order regressions and artificial neural networks. Cement Concrete Composites 29(6), 474\u2013480 (2007)","journal-title":"Cement Concrete Composites"},{"issue":"2","key":"23_CR21","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1109\/TPAMI.2017.2688363","volume":"40","author":"X Zhen","year":"2017","unstructured":"Zhen, X., Yu, M., He, X., Li, S.: Multi-target regression via robust low-rank learning. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 497\u2013504 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Communications in Computer and Information Science","ECML PKDD 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65965-3_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:37:12Z","timestamp":1724409432000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-65965-3_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030659646","9783030659653"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65965-3_23","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"2 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd2020.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"945","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"195","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}