{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T01:46:36Z","timestamp":1755999996749,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030590505"},{"type":"electronic","value":"9783030590512"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59051-2_14","type":"book-chapter","created":{"date-parts":[[2020,9,12]],"date-time":"2020-09-12T19:02:51Z","timestamp":1599937371000},"page":"213-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MUEnsemble: Multi-ratio Undersampling-Based Ensemble Framework for Imbalanced Data"],"prefix":"10.1007","author":[{"given":"Takahiro","family":"Komamizu","sequence":"first","affiliation":[]},{"given":"Risa","family":"Uehara","sequence":"additional","affiliation":[]},{"given":"Yasuhiro","family":"Ogawa","sequence":"additional","affiliation":[]},{"given":"Katsuhiko","family":"Toyama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"14_CR1","unstructured":"Bao, H., Sugiyama, M.: Calibrated surrogate maximization of linear-fractional utility in binary classification. CoRR abs\/1905.12511 (2019). http:\/\/arxiv.org\/abs\/1905.12511"},{"key":"14_CR2","unstructured":"Batista, G.E.A.P.A., Bazzan, A.L.C., Monard, M.C.: Balancing training data for automated annotation of keywords: a case study. In: II Brazilian Workshop on Bioinformatics, pp. 10\u201318 (2003)"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. 6(1), 20\u201329 (2004)","DOI":"10.1145\/1007730.1007735"},{"issue":"3","key":"14_CR4","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1007\/s10115-019-01380-z","volume":"62","author":"C Bellinger","year":"2019","unstructured":"Bellinger, C., Sharma, S., Japkowicz, N., Za\u00efane, O.R.: Framework for extreme imbalance classification: SWIM\u2014sampling with the majority class. Knowl. Inf. Syst. 62(3), 841\u2013866 (2019). https:\/\/doi.org\/10.1007\/s10115-019-01380-z","journal-title":"Knowl. Inf. Syst."},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Bhattacharya, S., Rajan, V., Shrivastava, H.: ICU mortality prediction: a classification algorithm for imbalanced datasets. In: AAAI 2017, pp. 1288\u20131294 (2017)","DOI":"10.1609\/aaai.v31i1.10721"},{"key":"14_CR6","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth (1984)"},{"key":"14_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"14_CR8","unstructured":"Dua, D., Graff, C.: UCI Machine Learning Repository (2019). http:\/\/archive.ics.uci.edu\/ml"},{"key":"14_CR9","first-page":"973","volume":"2001","author":"C Elkan","year":"2001","unstructured":"Elkan, C.: The foundations of cost-sensitive learning. IJCAI 2001, 973\u2013978 (2001)","journal-title":"IJCAI"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Galar, M., Fern\u00e1ndez, A., Tartas, E.B., Sola, H.B., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C 42(4), 463\u2013484 (2012)","DOI":"10.1109\/TSMCC.2011.2161285"},{"key":"14_CR11","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.neucom.2018.07.091","volume":"343","author":"SE G\u00f3mez","year":"2019","unstructured":"G\u00f3mez, S.E., Hern\u00e1ndez-Callejo, L., Mart\u00ednez, B.C., S\u00e1nchez-Esguevillas, A.J.: Exploratory study on class imbalance and solutions for network traffic classification. Neurocomputing 343, 100\u2013119 (2019)","journal-title":"Neurocomputing"},{"key":"14_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in Intelligent Computing","author":"H Han","year":"2005","unstructured":"Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878\u2013887. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11538059_91"},{"key":"14_CR13","first-page":"1322","volume":"2008","author":"H He","year":"2008","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. IJCNN 2008, 1322\u20131328 (2008)","journal-title":"IJCNN"},{"issue":"1","key":"14_CR14","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1007730.1007737","volume":"6","author":"T Jo","year":"2004","unstructured":"Jo, T., Japkowicz, N.: Class imbalances versus small disjuncts. SIGKDD Explor. 6(1), 40\u201349 (2004)","journal-title":"SIGKDD Explor."},{"key":"14_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1007\/11893028_93","volume-title":"Neural Information Processing","author":"P Kang","year":"2006","unstructured":"Kang, P., Cho, S.: EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems. In: King, I., Wang, J., Chan, L.-W., Wang, D.L. (eds.) ICONIP 2006. LNCS, vol. 4232, pp. 837\u2013846. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11893028_93"},{"key":"14_CR16","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.asoc.2013.08.014","volume":"14","author":"B Krawczyk","year":"2014","unstructured":"Krawczyk, B., Wozniak, M., Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14, 554\u2013562 (2014)","journal-title":"Appl. Soft Comput."},{"key":"14_CR17","unstructured":"Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: ICML 1997, pp. 179\u2013186 (1997)"},{"issue":"17","key":"14_CR18","first-page":"1","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1\u20135 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Liu, X., Wu, J., Zhou, Z.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B 39(2), 539\u2013550 (2009)","DOI":"10.1109\/TSMCB.2008.2007853"},{"key":"14_CR20","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.jss.2017.07.006","volume":"132","author":"W Lu","year":"2017","unstructured":"Lu, W., Li, Z., Chu, J.: Adaptive ensemble undersampling-boost: a novel learning framework for imbalanced data. J. Syst. Softw. 132, 272\u2013282 (2017)","journal-title":"J. Syst. Softw."},{"issue":"10","key":"14_CR21","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1007\/s00500-010-0625-8","volume":"15","author":"J Luengo","year":"2011","unstructured":"Luengo, J., Fern\u00e1ndez, A., Garc\u00eda, S., Herrera, F.: Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling. Soft. Comput. 15(10), 1909\u20131936 (2011)","journal-title":"Soft. Comput."},{"key":"14_CR22","unstructured":"Mani, I., Zhang, I.: kNN approach to unbalanced data distributions: a case study involving information extraction. In: ICML 2003 Workshop on Learning from Imbalanced Datasets, vol. 126 (2003)"},{"issue":"1","key":"14_CR23","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1504\/IJKESDP.2011.039875","volume":"3","author":"HM Nguyen","year":"2011","unstructured":"Nguyen, H.M., Cooper, E.W., Kamei, K.: Borderline over-sampling for imbalanced data classification. IJKESDP 3(1), 4\u201321 (2011)","journal-title":"IJKESDP"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Peng, M., et al.: Trainable undersampling for class-imbalance learning. In: AAAI 2019, pp. 4707\u20134714 (2019)","DOI":"10.1609\/aaai.v33i01.33014707"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Pozzolo, A.D., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: SSCI 2015, pp. 159\u2013166 (2015)","DOI":"10.1109\/SSCI.2015.33"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, D., Herraiz, I., Harrison, R., Dolado, J.J., Riquelme, J.C.: Preliminary comparison of techniques for dealing with imbalance in software defect prediction. In: EASE 2014, pp. 43:1\u201343:10 (2014)","DOI":"10.1145\/2601248.2601294"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Seiffert, C., Khoshgoftaar, T.M., Hulse, J.V., Napolitano, A.: RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans. Syst. Man Cybern. Part A 40(1), 185\u2013197 (2010)","DOI":"10.1109\/TSMCA.2009.2029559"},{"key":"14_CR28","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.compag.2019.03.006","volume":"159","author":"A Sharififar","year":"2019","unstructured":"Sharififar, A., Sarmadian, F., Minasny, B.: Mapping imbalanced soil classes using Markov chain random fields models treated with data resampling technique. Comput. Electron. Agric. 159, 110\u2013118 (2019)","journal-title":"Comput. Electron. Agric."},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Sharma, S., Bellinger, C., Krawczyk, B., Za\u00efane, O.R., Japkowicz, N.: Synthetic oversampling with the majority class: a new perspective on handling extreme imbalance. In: ICDM 2018, pp. 447\u2013456 (2018)","DOI":"10.1109\/ICDM.2018.00060"},{"issue":"2","key":"14_CR30","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10994-013-5422-z","volume":"95","author":"MR Smith","year":"2013","unstructured":"Smith, M.R., Martinez, T., Giraud-Carrier, C.: An instance level analysis of data complexity. Mach. Learn. 95(2), 225\u2013256 (2013). https:\/\/doi.org\/10.1007\/s10994-013-5422-z","journal-title":"Mach. Learn."},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. SMC-6(11), 769\u2013772 (1976)","DOI":"10.1109\/TSMC.1976.4309452"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Wang, H., Gao, Y., Shi, Y., Wang, H.: A fast distributed classification algorithm for large-scale imbalanced data. In: ICDM 2016, pp. 1251\u20131256 (2016)","DOI":"10.1109\/ICDM.2016.0168"},{"issue":"3","key":"14_CR33","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"2","author":"DL Wilson","year":"1972","unstructured":"Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. 2(3), 408\u2013421 (1972)","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59051-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:37:55Z","timestamp":1709811475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59051-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030590505","9783030590512"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59051-2_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"8 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","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":"17 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dexa.org\/dexa2020","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":"190","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":"38","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":"20","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":"20% - 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":"4-6","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":"3-4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online. DEXA Workshops volume: submissions sent - 15, full papers accepted - 6, short papers accepted - 4, reviewers per paper 3, papers per reviewer 1-2","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)"}}]}}