{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:04:46Z","timestamp":1740117886710,"version":"3.37.3"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1016\/j.neucom.2020.12.004","type":"journal-article","created":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T02:55:52Z","timestamp":1608173752000},"page":"232-247","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":15,"special_numbering":"C","title":["Multi-label thresholding for cost-sensitive classification"],"prefix":"10.1016","volume":"436","author":[{"given":"Reem","family":"Alotaibi","sequence":"first","affiliation":[]},{"given":"Peter","family":"Flach","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2020.12.004_b0005","doi-asserted-by":"crossref","unstructured":"G. Tsoumakas, I. Katakis, I. Vlahavas, Mining multi-label data, in: Data Mining and Knowledge Discovery Handbook, 2010, pp. 667\u2013685.","DOI":"10.1007\/978-0-387-09823-4_34"},{"issue":"8","key":"10.1016\/j.neucom.2020.12.004_b0010","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","article-title":"A review on multi-label learning algorithms","volume":"26","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2020.12.004_b0015","doi-asserted-by":"crossref","unstructured":"E. Gibaja, S. Ventura, A tutorial on multilabel learning, ACM Comput. Surveys 47 (3) (2015) 52:1\u201352:38. ISSN 0360-0300, http:\/\/doi.acm.org\/10.1145\/2716262.","DOI":"10.1145\/2716262"},{"key":"10.1016\/j.neucom.2020.12.004_b0020","unstructured":"R. Al-Otaibi, M. Kull, P. Flach, Declaratively capturing local label correlations with multi-label trees, in: G.A. Kaminka, M. Fox, P. Bouquet, E. H\u00fcllermeier, V. Dignum, F. Dignum, F. van Harmelen (Eds.), Proceedings of the 22nd Biennial European Conference on Artificial Intelligence (ECAI2016), Including Prestigious Applications of Intelligent Systems (PAIS-2016), Vol. 285 of Frontiers in Artificial Intelligence and Applications, IOS press, pp. 1467\u20131475, http:\/\/ebooks.iospress.com\/volumearticle\/44904, 2016."},{"key":"10.1016\/j.neucom.2020.12.004_b0025","unstructured":"E.K. Yapp, X. Li, W.F. Lu, P.S. Tan, Comparison of base classifiers for multi-label learning, Neurocomputing. ISSN 0925-2312."},{"key":"10.1016\/j.neucom.2020.12.004_b0030","unstructured":"R. Al-Otaibi, P.A. Flach, M. Kull, Multi-label classification: a comparative study on threshold selection methods, 2014."},{"key":"10.1016\/j.neucom.2020.12.004_b0035","doi-asserted-by":"crossref","unstructured":"A. Rivolli, A. de Carvalho, The utiml package: multi-label classification in R, R J. 10 (2019) 24. 10.32614\/RJ-2018-041.","DOI":"10.32614\/RJ-2018-041"},{"key":"10.1016\/j.neucom.2020.12.004_b0040","unstructured":"C.X. Ling, V.S. Sheng, Cost-Sensitive Learning and the Class Imbalance Problem, 2008, Springer, pp. 869\u2013875. ISBN 978-0-387-30768-8, http:\/\/cling.csd.uwo.ca\/papers\/cost_sensitive.pdf."},{"key":"10.1016\/j.neucom.2020.12.004_b0045","unstructured":"C. Elkan, The foundations of cost-sensitive learning, in: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), 2001, pp. 973\u2013978."},{"key":"10.1016\/j.neucom.2020.12.004_b0050","unstructured":"H.-T. Lin, Cost-sensitive classification: status and beyond, in: Proceedings of Workshop Machine Learning Research in Taiwan: Challenges and Directions, 2010."},{"key":"10.1016\/j.neucom.2020.12.004_b0055","unstructured":"Z.-H. Zhou, X.-Y. Liu, On multi-class cost-sensitive learning, in: Proceedings of the 21st National Conference on Artificial Intelligence, 2006, AAAI Press, pp. 567\u2013572. ISBN 978-1-57735-281-5, http:\/\/dl.acm.org\/citation.cfm?id=1597538.1597630, 2006."},{"year":"2007","series-title":"Cost-sensitive Learning and the Class Imbalanced Problem","author":"Ling","key":"10.1016\/j.neucom.2020.12.004_b0060"},{"key":"10.1016\/j.neucom.2020.12.004_b0065","doi-asserted-by":"crossref","unstructured":"J. Li, X. Li, X. Yao, Cost-sensitive classification with genetic programming, in: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, 2005, vol. 3, IEEE Press, pp. 2114\u20132121. ISBN 0-7803-9363-5, http:\/\/www.cs.bham.ac.uk\/ xin\/papers\/LiLiYaoCEC05.pdf.","DOI":"10.1109\/CEC.2005.1554956"},{"key":"10.1016\/j.neucom.2020.12.004_b0070","doi-asserted-by":"crossref","unstructured":"N. Cesa-Bianchi, M. Re, G. Valentini, Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference, Mach. Learn. 88 (1) (2012) 209\u2013241. ISSN 1573-0565, doi: 10.1007\/s10994-011-5271-6.","DOI":"10.1007\/s10994-011-5271-6"},{"key":"10.1016\/j.neucom.2020.12.004_b0075","unstructured":"C. Li, H. Lin, Condensed filter tree for cost-sensitive multi-label classification, in: Proceedings of the 31th International Conference on Machine Learning, ICML 2014, 2014, Beijing, China, 21\u201326 June 2014, pp. 423\u2013431. http:\/\/jmlr.org\/proceedings\/papers\/v32\/lia14.html."},{"key":"10.1016\/j.neucom.2020.12.004_b0080","doi-asserted-by":"crossref","unstructured":"Y.-P. Wu, H.-T. Lin, Progressive random k-labelsets for cost-sensitive multi-label classification, Mach. Learn. (2016) 1\u201324. ISSN 1573-0565, doi: 10.1007\/s10994-016-5600-x.","DOI":"10.1007\/s10994-016-5600-x"},{"key":"10.1016\/j.neucom.2020.12.004_b0085","doi-asserted-by":"crossref","unstructured":"H.-Y. Lo, J.-C. Wang, H.-M. Wang, S.-D. Lin, Cost-sensitive multi-label learning for audio tag annotation and retrieval, IEEE Trans. Multimedia 13 (3) (2011) 518\u2013529. http:\/\/dblp.uni-trier.de\/db\/journals\/tmm\/tmm13.html#LoWWL11.","DOI":"10.1109\/TMM.2011.2129498"},{"key":"10.1016\/j.neucom.2020.12.004_b0090","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Networks"},{"key":"10.1016\/j.neucom.2020.12.004_b0095","doi-asserted-by":"crossref","unstructured":"G. Tsoumakas, I. Vlahavas, Random k-Labelsets: an ensemble method for multilabel classification, in: Proceedings of the 18th European Conference on Machine Learning, ECML07, 2007, Springer-Verlag, Berlin, Heidelberg, pp. 406\u2013417. ISBN 978-3-540-74957-8. doi: 10.1007\/978-3-540-74958-5_38.","DOI":"10.1007\/978-3-540-74958-5_38"},{"key":"10.1016\/j.neucom.2020.12.004_b0100","doi-asserted-by":"crossref","unstructured":"P. Cao, X. Liu, D. Zhao, O. Zaiane, Cost Sensitive Ranking Support Vector Machine for Multi-label Data Learning, in: A. Abraham, A. Haqiq, A.M. Alimi, G. Mezzour, N. Rokbani, A.K. Muda (Eds.), Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016), 2017, Springer International Publishing, Cham, pp. 244\u2013255. ISBN 978-3-319-52941-7.","DOI":"10.1007\/978-3-319-52941-7_25"},{"key":"10.1016\/j.neucom.2020.12.004_b0105","doi-asserted-by":"crossref","unstructured":"K.-H. Huang, H.-T. Lin, Cost-sensitive label embedding for multi-label classification, Mach. Learn. 106 (9) (2017) 1725\u20131746. ISSN 1573-0565, doi: 10.1007\/s10994-017-5659-z.","DOI":"10.1007\/s10994-017-5659-z"},{"key":"10.1016\/j.neucom.2020.12.004_b0110","doi-asserted-by":"crossref","unstructured":"C.-Y. Hsieh, Y.-A. Lin, H.-T. Lin, A deep model with local surrogate loss for general cost-sensitive multi-label learning, in: AAAI, 2018.","DOI":"10.1609\/aaai.v32i1.11816"},{"key":"10.1016\/j.neucom.2020.12.004_b0115","unstructured":"R.-E. Fan, C.-J. Lin, A Study on Threshold Selection for Multi-label Classification, Tech. Rep., National Taiwan University, 2007. http:\/\/www.csie.ntu.edu.tw\/cjlin\/papers\/threshold.pdf."},{"key":"10.1016\/j.neucom.2020.12.004_b0120","doi-asserted-by":"crossref","unstructured":"L. Tang, S. Rajan, V.K. Narayanan, Large scale multi-label classification via metalabeler, in: Proceedings of the 18th International Conference on World Wide Web, WWW09, 2009, ACM, New York, NY, USA, pp. 211\u2013220. ISBN 978-1-60558-487-4, http:\/\/doi.acm.org\/10.1145\/1526709.1526738.","DOI":"10.1145\/1526709.1526738"},{"key":"10.1016\/j.neucom.2020.12.004_b0125","doi-asserted-by":"crossref","unstructured":"I. Triguero, C. Vens, Labelling strategies for hierarchical multi-label classification techniques, Pattern Recogn. 56 (2016) 170\u2013183. ISSN 0031-3203, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0031320316000881.","DOI":"10.1016\/j.patcog.2016.02.017"},{"key":"10.1016\/j.neucom.2020.12.004_b0130","unstructured":"J. Hern\u00e1ndez-Orallo, P. Flach, C. Ferri, A unified view of performance metrics: translating threshold choice into expected classification loss, J. Mach. Learn. Res. 13 (1) (2012) 2813\u20132869. ISSN 1532-4435, http:\/\/dl.acm.org\/citation.cfm?id=2503308.2503332."},{"key":"10.1016\/j.neucom.2020.12.004_b0135","unstructured":"P. Flach, Classification in context: adapting to changes in class and cost distribution, in: First International Workshop on Learning over Multiple Contexts (LMCE) at ECML-PKDD 2014, 2014, Nancy, France, http:\/\/users.dsic.upv.es\/ flip\/LMCE2014\/Papers\/lmce2014_submission_18.pdf."},{"key":"10.1016\/j.neucom.2020.12.004_b0140","first-page":"1","article-title":"Multi-label classification: an overview","volume":"2007","author":"Tsoumakas","year":"2007","journal-title":"Int. J. Data Warehousing Min."},{"key":"10.1016\/j.neucom.2020.12.004_b0145","unstructured":"M.S. Sorower, A literature survey on algorithms for multi-label learning, Tech. Rep., Oregon State University, 2010."},{"issue":"4","key":"10.1016\/j.neucom.2020.12.004_b0150","first-page":"303","article-title":"Binary relevance efficacy for multilabel classification","volume":"1","author":"Luaces","year":"2012","journal-title":"Prog. AI"},{"key":"10.1016\/j.neucom.2020.12.004_b0155","doi-asserted-by":"crossref","unstructured":"G. Madjarov, D. Kocev, D. Gjorgjevikj, S. D\u017eeroski, An extensive experimental comparison of methods for multi-label learning, Pattern Recogn. 45 (9) (2012) 3084\u20133104. ISSN 0031-3203, doi: 10.1016\/j.patcog.2012.03.004.","DOI":"10.1016\/j.patcog.2012.03.004"},{"key":"10.1016\/j.neucom.2020.12.004_b0160","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1023\/A:1009982220290","article-title":"An evaluation of statistical approaches to text categorization","volume":"1","author":"Yang","year":"1999","journal-title":"J. Inf. Retrieval"},{"key":"10.1016\/j.neucom.2020.12.004_b0165","doi-asserted-by":"crossref","unstructured":"S. Godbole, S. Sarawagi, Discriminative methods for multi-labeled classification, in: Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2004, pp. 22\u201330.","DOI":"10.1007\/978-3-540-24775-3_5"},{"key":"10.1016\/j.neucom.2020.12.004_b0170","doi-asserted-by":"crossref","unstructured":"N. Ghamrawi, A. McCallum, Collective multi-label classification, in: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM05, ACM, New York, NY, USA, 2005, pp. 195\u2013200. ISBN 1-59593-140-6, http:\/\/doi.acm.org\/10.1145\/1099554.1099591.","DOI":"10.1145\/1099554.1099591"},{"key":"10.1016\/j.neucom.2020.12.004_b0175","doi-asserted-by":"crossref","unstructured":"R.E. Schapire, Y. Singer, Improved boosting algorithms using confidence-rated predictions, in: Machine Learning, 1999, pp. 297\u2013336. ISSN 1573-0565, https:\/\/doi.org\/10.1023\/A:1007614523901.","DOI":"10.1023\/A:1007614523901"},{"key":"10.1016\/j.neucom.2020.12.004_b0180","doi-asserted-by":"crossref","unstructured":"J. Read, B. Pfahringer, G. Holmes, E. Frank, Classifier chains for multi-label classification, in: Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD09), Lecture Notes in Computer Science, Springer-Verlag, Berlin, Heidelberg, 2009, pp. 254\u2013269. ISBN 978-3-642-04173-0, doi: 10.1007\/978-3-642-04174-7_17.","DOI":"10.1007\/978-3-642-04174-7_17"},{"key":"10.1016\/j.neucom.2020.12.004_b0185","doi-asserted-by":"crossref","unstructured":"J.A. Fernandes, J.A. Lozano, I. n. Inza, X. Irigoien, A. P\u00e9rez, J.D. Rodr\u00edguez, Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting, Environ. Model. Software 40 (2013) 245\u2013254. ISSN 1364\u20138152, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364815212002472.","DOI":"10.1016\/j.envsoft.2012.10.001"},{"key":"10.1016\/j.neucom.2020.12.004_b0190","doi-asserted-by":"crossref","unstructured":"E. Hadavandi, J. Shahrabi, Y. Hayashi, SPMoE: a novel subspace-projected mixture of experts model for multi-target regression problems (2015) 1\u201319. doi: 10.1007\/s00500-015-1623-7.","DOI":"10.1007\/s00500-015-1623-7"},{"key":"10.1016\/j.neucom.2020.12.004_b0195","doi-asserted-by":"crossref","unstructured":"Y. Yang, A study of thresholding strategies for text categorization, in: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR01, 2001, ACM, New York, NY, USA, pp. 137\u2013145. ISBN 1-58113-331-6, http:\/\/doi.acm.org\/10.1145\/383952.383975.","DOI":"10.1145\/383952.383975"},{"key":"10.1016\/j.neucom.2020.12.004_b0200","doi-asserted-by":"crossref","unstructured":"C. Largeron, C. Moulin, M. G\u00e9ry, MCut: a thresholding strategy for multi-label classification, in: J. Hollm\u00e9n, F. Klawonn, A. Tucker (Eds.), Proceedings of the 11th International Symposium on Advances in Intelligent Data Analysis, vol. 7619 of Lecture Notes in Computer Science, 2012, Springer, pp. 172\u2013183. ISBN 978-3-642-34155-7, http:\/\/dblp.uni-trier.de\/db\/conf\/ida\/ida2012.html#LargeronMG12.","DOI":"10.1007\/978-3-642-34156-4_17"},{"key":"10.1016\/j.neucom.2020.12.004_b0205","doi-asserted-by":"crossref","unstructured":"C. Drummond, R.C. Holte, Cost curves: an improved method for visualizing classifier performance, Mach. Learn. 65 (1) (2006) 95\u2013130. http:\/\/dblp.uni-trier.de\/db\/journals\/ml\/ml65.html#DrummondH06.","DOI":"10.1007\/s10994-006-8199-5"},{"key":"10.1016\/j.neucom.2020.12.004_b0210","unstructured":"J. Hern\u00e1ndez-Orallo, P. Flach, C. Ferri, Brier curves: a new cost-based visualisation of classifier performance, in: L. Getoor, T. Scheffer (Eds.), Proceedings of the 28th International Conference on Machine Learning (ICML11, ICML11), 2011, Omnipress, pp. 585\u2013592. http:\/\/dblp.uni-trier.de\/db\/conf\/icml\/icml2011.html#Hernandez-OralloFR11."},{"key":"10.1016\/j.neucom.2020.12.004_b0215","unstructured":"G. Tsoumakas, E. Spyromitros-Xioufis, J. Vilcek, I. Vlahavas, MULAN: a Java library for multi-label learning, J. Mach. Learn. Res. 12 (2011) 2411\u20132414. ISSN 1532\u20134435, http:\/\/dl.acm.org\/citation.cfm?id=1953048.2021078."},{"key":"10.1016\/j.neucom.2020.12.004_b0220","unstructured":"P. Flach, J. Hern\u00e1ndez-Orallo, C. Ferri, A coherent interpretation of AUC as a measure of aggregated classification performance, in: L. Getoor, T. Scheffer (Eds.), Proceedings of the 28th International Conference on Machine Learning (ICML11), 2011."},{"key":"10.1016\/j.neucom.2020.12.004_b0225","unstructured":"J. Dem\u0161ar, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res. 7 (2006) 1\u201330. ISSN 1532\u20134435, http:\/\/dl.acm.org\/citation.cfm?id=1248547.1248548."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220318853?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220318853?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T09:38:57Z","timestamp":1724060337000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231220318853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5]]},"references-count":45,"alternative-id":["S0925231220318853"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2020.12.004","relation":{},"ISSN":["0925-2312"],"issn-type":[{"type":"print","value":"0925-2312"}],"subject":[],"published":{"date-parts":[[2021,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-label thresholding for cost-sensitive classification","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2020.12.004","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}