{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T22:40:10Z","timestamp":1747089610742,"version":"3.40.5"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030488604"},{"type":"electronic","value":"9783030488611"}],"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\/978-3-030-48861-1_6","type":"book-chapter","created":{"date-parts":[[2020,5,13]],"date-time":"2020-05-13T14:17:30Z","timestamp":1589379450000},"page":"85-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Soft Voting Windowing Ensembles for Learning from Partially Labelled Streams"],"prefix":"10.1007","author":[{"given":"Sean L. A.","family":"Floyd","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Herna L.","family":"Viktor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,14]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443\u2013448. SIAM (2007)","DOI":"10.1137\/1.9781611972771.42"},{"key":"6_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-3-642-15880-3_15","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"A Bifet","year":"2010","unstructured":"Bifet, A., Holmes, G., Pfahringer, B.: Leveraging bagging for evolving data streams. In: Balc\u00e1zar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 135\u2013150. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15880-3_15"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Bifet, A., et al.: New ensemble methods for evolving data streams. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 139\u2013148. ACM (2009)","DOI":"10.1145\/1557019.1557041"},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1109\/TC.2013.13","volume":"63","author":"G Creech","year":"2014","unstructured":"Creech, G., Hu, J.: A semantic approach to host-based intrusion detection systems using contiguous and discontinuous system call patterns. IEEE Trans. Comput. 63, 807\u2013819 (2014)","journal-title":"IEEE Trans. Comput."},{"key":"6_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-67786-6_1","volume-title":"Discovery Science","author":"S D\u2019Ettorre","year":"2017","unstructured":"D\u2019Ettorre, S., Viktor, H.L., Paquet, E.: Context-based abrupt change detection and adaptation for categorical data streams. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds.) DS 2017. LNCS (LNAI), vol. 10558, pp. 3\u201317. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67786-6_1"},{"key":"6_CR6","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511973000","volume-title":"Machine Learning: The Art and Science of Algorithms that Make Sense of Data","author":"P Flach","year":"2012","unstructured":"Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge (2012)"},{"key":"6_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-540-28645-5_29","volume-title":"Advances in Artificial Intelligence \u2013 SBIA 2004","author":"J Gama","year":"2004","unstructured":"Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286\u2013295. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-28645-5_29"},{"key":"6_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/978-3-319-18032-8_30","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"A Haque","year":"2015","unstructured":"Haque, A., Khan, L., Baron, M.: Semi supervised adaptive framework for classifying evolving data stream. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 383\u2013394. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-18032-8_30"},{"key":"6_CR9","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511921803","volume-title":"Evaluating Learning Algorithms: A Classification Perspective","author":"N Japkowicz","year":"2011","unstructured":"Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, Cambridge (2011)"},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","volume":"37","author":"B Krawczyk","year":"2017","unstructured":"Krawczyk, B., et al.: Ensemble learning for data stream analysis: a survey. Inf. Fusion 37, 132\u2013156 (2017). ISSN 1566-2535","journal-title":"Inf. Fusion"},{"issue":"1","key":"6_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2674026.2674028","volume":"16","author":"G Krempl","year":"2014","unstructured":"Krempl, G., et al.: Open challenges for data stream mining research. ACM SIGKDD Explor. Newsl. 16(1), 1\u201310 (2014)","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Nishida, K., Yamauchi, K.: Adaptive classifiers-ensemble system for tracking concept drift. In: 2007 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3607\u20133612. IEEE (2007)","DOI":"10.1109\/ICMLC.2007.4370772"},{"issue":"11","key":"6_CR13","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1007\/s10994-018-5719-z","volume":"107","author":"A Pesaranghader","year":"2018","unstructured":"Pesaranghader, A., Viktor, H., Paquet, E.: Reservoir of diverse adaptive learners and stacking fast Hoeffding drift detection methods for evolving data streams. Mach. Learn. 107(11), 1711\u20131743 (2018). https:\/\/doi.org\/10.1007\/s10994-018-5719-z","journal-title":"Mach. Learn."},{"issue":"4","key":"6_CR14","first-page":"462","volume":"19","author":"P Sobolewski","year":"2013","unstructured":"Sobolewski, P., Wozniak, M.: Concept drift detection and model selection with simulated recurrence and ensembles of statistical detectors. J. Univ. Comput. Sci. 19(4), 462\u2013483 (2013)","journal-title":"J. Univ. Comput. Sci."},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Street, W.N., Kim, Y.S.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377\u2013382. ACM (2001)","DOI":"10.1145\/502512.502568"},{"issue":"1","key":"6_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00196ED1V01Y200906AIM006","volume":"3","author":"X Zhu","year":"2009","unstructured":"Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1\u2013130 (2009)","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"issue":"3","key":"6_CR17","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s10994-014-5441-4","volume":"98","author":"I \u017dliobait\u0117","year":"2014","unstructured":"\u017dliobait\u0117, I., Bifet, A., Read, J., Pfahringer, B., Holmes, G.: Evaluation methods and decision theory for classification of streaming data with temporal dependence. Mach. Learn. 98(3), 455\u2013482 (2014). https:\/\/doi.org\/10.1007\/s10994-014-5441-4","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","New Frontiers in Mining Complex Patterns"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-48861-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T22:02:52Z","timestamp":1747087372000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-48861-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030488604","9783030488611"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-48861-1_6","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":"14 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NFMCP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on New Frontiers in Mining Complex Patterns","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nfmcp2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.di.uniba.it\/~loglisci\/NFMCP2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"18","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":"9","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":"50% - 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":"2,5","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":"2,9","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)"}}]}}