{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T14:10:56Z","timestamp":1768313456802,"version":"3.49.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030608835","type":"print"},{"value":"9783030608842","type":"electronic"}],"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\/978-3-030-60884-2_5","type":"book-chapter","created":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T23:04:50Z","timestamp":1602025490000},"page":"65-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Outliers Detection in Multi-label Datasets"],"prefix":"10.1007","author":[{"given":"Marilyn","family":"Bello","sequence":"first","affiliation":[]},{"given":"Gonzalo","family":"N\u00e1poles","sequence":"additional","affiliation":[]},{"given":"Rafael","family":"Morera","sequence":"additional","affiliation":[]},{"given":"Koen","family":"Vanhoof","sequence":"additional","affiliation":[]},{"given":"Rafael","family":"Bello","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"key":"5_CR1","unstructured":"Acu\u00f1a, E., Rodriguez, C.: On Detection of Outliers and Their Effect in Supervised Classification, vol. 15. University of Puerto Rico at Mayaguez (2004)"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-3-319-14142-8_8","volume-title":"Data Mining","author":"CC Aggarwal","year":"2015","unstructured":"Aggarwal, C.C.: Outlier analysis. Data Mining, pp. 237\u2013263. Springer, Cham (2015). \nhttps:\/\/doi.org\/10.1007\/978-3-319-14142-8_8"},{"key":"5_CR3","unstructured":"Barnet, V., Lewis, T.: Outliers in Statistical Data (1994)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587510"},{"issue":"4","key":"5_CR5","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1023\/A:1020499411651","volume":"5","author":"A Bookstein","year":"2002","unstructured":"Bookstein, A., Kulyukin, V.A., Raita, T.: Generalized hamming distance. Inf. Retrieval 5(4), 353\u2013375 (2002)","journal-title":"Inf. Retrieval"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/335191.335388"},{"key":"5_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/978-3-319-32034-2_41","volume-title":"Hybrid Artificial Intelligent Systems","author":"F Charte","year":"2016","unstructured":"Charte, F., Charte, D., Rivera, A., del Jesus, M.J., Herrera, F.: R ultimate multilabel dataset repository. In: Mart\u00ednez-\u00c1lvarez, F., Troncoso, A., Quinti\u00e1n, H., Corchado, E. (eds.) HAIS 2016. LNCS (LNAI), vol. 9648, pp. 487\u2013499. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-32034-2_41"},{"issue":"12","key":"5_CR8","doi-asserted-by":"publisher","first-page":"8745","DOI":"10.1016\/j.eswa.2010.06.040","volume":"37","author":"Y Chen","year":"2010","unstructured":"Chen, Y., Miao, D., Zhang, H.: Neighborhood outlier detection. Expert Syst. Appl. 37(12), 8745\u20138749 (2010)","journal-title":"Expert Syst. Appl."},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Gebhardt, J., Goldstein, M., Shafait, F., Dengel, A.: Document authentication using printing technique features and unsupervised anomaly detection. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 479\u2013483. IEEE (2013)","DOI":"10.1109\/ICDAR.2013.102"},{"key":"5_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-3994-4","volume-title":"Identification of Outliers","author":"DM Hawkins","year":"1980","unstructured":"Hawkins, D.M.: Identification of Outliers, vol. 11. Springer, Netherlands (1980). \nhttps:\/\/doi.org\/10.1007\/978-94-015-3994-4"},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-319-41111-8_2","volume-title":"Multilabel Classification","author":"F Herrera","year":"2016","unstructured":"Herrera, F., Charte, F., Rivera, A.J., del Jesus, M.J.: Multilabel classification. Multilabel Classification, pp. 17\u201331. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-41111-8_2"},{"issue":"3","key":"5_CR12","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264\u2013323 (1999)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"2","key":"5_CR13","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s10489-014-0591-4","volume":"42","author":"F Jiang","year":"2014","unstructured":"Jiang, F., Chen, Y.-M.: Outlier detection based on granular computing and rough set theory. Appl. Intell. 42(2), 303\u2013322 (2014). \nhttps:\/\/doi.org\/10.1007\/s10489-014-0591-4","journal-title":"Appl. Intell."},{"key":"5_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/11548706_9","volume-title":"Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing","author":"F Jiang","year":"2005","unstructured":"Jiang, F., Sui, Y., Cao, C.: Outlier detection using rough set theory. In: \u015al\u0119zak, D., Yao, J.T., Peters, J.F., Ziarko, W., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 79\u201387. Springer, Heidelberg (2005). \nhttps:\/\/doi.org\/10.1007\/11548706_9"},{"issue":"5","key":"5_CR15","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1080\/03081070701251182","volume":"37","author":"F Jiang","year":"2008","unstructured":"Jiang, F., Sui, Y., Cao, C.: A rough set approach to outlier detection. Int. J. Gener. Syst. 37(5), 519\u2013536 (2008)","journal-title":"Int. J. Gener. Syst."},{"key":"5_CR16","unstructured":"Johnson, T., Kwok, I., Ng, R.T.: Fast computation of 2-dimensional depth contours. In: KDD, pp. 224\u2013228. Citeseer (1998)"},{"issue":"3\u20134","key":"5_CR17","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s007780050006","volume":"8","author":"EM Knorr","year":"2000","unstructured":"Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-based outliers: algorithms and applications. VLDB J. 8(3\u20134), 237\u2013253 (2000)","journal-title":"VLDB J."},{"key":"5_CR18","unstructured":"Kov\u00e1cs, L., Vass, D., Vid\u00e1cs, A.: Improving quality of service parameter prediction with preliminary outlier detection and elimination. In: Proceedings of the Second International Workshop on Inter-domain Performance and Simulation (IPS 2004), Budapest, vol. 2004, pp. 194\u2013199 (2004)"},{"key":"5_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/3-540-36159-6_23","volume-title":"Information and Communications Security","author":"E Lundin","year":"2002","unstructured":"Lundin, E., Kvarnstr\u00f6m, H., Jonsson, E.: A synthetic fraud data generation methodology. In: Deng, R., Bao, F., Zhou, J., Qing, S. (eds.) ICICS 2002. LNCS, vol. 2513, pp. 265\u2013277. Springer, Heidelberg (2002). \nhttps:\/\/doi.org\/10.1007\/3-540-36159-6_23"},{"issue":"5","key":"5_CR20","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341\u2013356 (1982)","journal-title":"Int. J. Comput. Inf. Sci."},{"issue":"3","key":"5_CR21","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.ipm.2018.01.002","volume":"54","author":"RB Pereira","year":"2018","unstructured":"Pereira, R.B., Plastino, A., Zadrozny, B., Merschmann, L.H.: Correlation analysis of performance measures for multi-label classification. Inf. Process. Manage. 54(3), 359\u2013369 (2018)","journal-title":"Inf. Process. Manage."},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Porwal, U., Mukund, S.: Credit card fraud detection in e-commerce: an outlier detection approach. arXiv preprint \narXiv:1811.02196\n\n (2018)","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00045"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Ramakrishnan, J., Shaabani, E., Li, C., Sustik, M.A.: Anomaly detection for an e-commerce pricing system. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1917\u20131926 (2019)","DOI":"10.1145\/3292500.3330748"},{"key":"5_CR24","volume-title":"Robust Regression and Outlier Detection","author":"PJ Rousseeuw","year":"2005","unstructured":"Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection, vol. 589. Wiley, New York (2005)"},{"issue":"2","key":"5_CR25","doi-asserted-by":"publisher","first-page":"191","DOI":"10.3233\/IDA-2009-0363","volume":"13","author":"F Shaari","year":"2009","unstructured":"Shaari, F., Bakar, A.A., Hamdan, A.R.: Outlier detection based on rough sets theory. Intell. Data Anal. 13(2), 191\u2013206 (2009)","journal-title":"Intell. Data Anal."},{"issue":"2","key":"5_CR26","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1109\/69.842271","volume":"12","author":"R Slowinski","year":"2000","unstructured":"Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity. IEEE Trans. Knowl. Data Eng. 12(2), 331\u2013336 (2000)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"Jul","key":"5_CR27","first-page":"2411","volume":"12","author":"G Tsoumakas","year":"2011","unstructured":"Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: Mulan: a java library for multi-label learning. J. Mach. Learn. Res. 12(Jul), 2411\u20132414 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/978-3-540-74958-5_38","volume-title":"Machine Learning: ECML 2007","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladeni\u010d, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406\u2013417. Springer, Heidelberg (2007). \nhttps:\/\/doi.org\/10.1007\/978-3-540-74958-5_38"},{"key":"5_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1613\/jair.346","volume":"6","author":"DR Wilson","year":"1997","unstructured":"Wilson, D.R., Martinez, T.R.: Improved heterogeneous distance functions. J. Artif. Intell. Res. 6, 1\u201334 (1997)","journal-title":"J. Artif. Intell. Res."},{"issue":"10","key":"5_CR30","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","volume":"18","author":"ML Zhang","year":"2006","unstructured":"Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338\u20131351 (2006)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7","key":"5_CR31","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038\u20132048 (2007)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Advances in Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60884-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T23:16:55Z","timestamp":1602026215000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-60884-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030608835","9783030608842"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60884-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","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":"12 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2020\/","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":"186","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":"77","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":"41% - 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":"3","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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}