{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:46:15Z","timestamp":1771613175281,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030625085","type":"print"},{"value":"9783030625092","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\/978-3-030-62509-2_11","type":"book-chapter","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:03:00Z","timestamp":1604275380000},"page":"125-136","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic Features Spaces and Machine Learning: Open Problems and Synthetic Data Sets"],"prefix":"10.1007","author":[{"given":"Sema Kayapinar","family":"Kaya","sequence":"first","affiliation":[]},{"given":"Guillermo","family":"Navarro-Arribas","sequence":"additional","affiliation":[]},{"given":"Vicen\u00e7","family":"Torra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,2]]},"reference":[{"key":"11_CR1","unstructured":"Abuzayed, N., Ergen\u00e7, B.: Dynamic itemset mining under multiple support thresholds. In: Proceedings of the FSDM 2016, pp. 141\u2013148 (2016)"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Abuzayed, N.N., Ergen\u00e7, B.: Comparison of dynamic itemset mining algorithms for multiple support thresholds. In: Proceedings of the IDEAS 2017 (2017)","DOI":"10.1145\/3105831.3105846"},{"key":"11_CR3","unstructured":"Belford, M., Mac Namee, B., Greene, D.: Synthetic dataset generation for online topic modeling. In: Proceedings of the AICS 2017, pp. 7\u20138 (2017)"},{"key":"11_CR4","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Drichlet allocation. J. Mach. Learn. Res. 3, 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"11_CR5","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1109\/TNNLS.2013.2271915","volume":"25","author":"JB Gomes","year":"2014","unstructured":"Gomes, J.B., Gaber, M., Sousa, P.A.C., Menasalvas, E.: Mining recurring concepts in a dynamic feature space. IEEE Trans. Neural Networks Learn. Syst. 25(1), 95\u2013110 (2014)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","volume":"29","author":"J Gubbi","year":"2013","unstructured":"Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29, 1645\u20131660 (2013)","journal-title":"Future Gener. Comput. Syst."},{"issue":"4","key":"11_CR7","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.inffus.2011.03.001","volume":"13","author":"J Herranz","year":"2012","unstructured":"Herranz, J., Nin, J., Sol\u00e9, M.: Kd-trees and the real disclosure risks of large statistical databases. Inf. Fusion 13(4), 260\u2013270 (2012)","journal-title":"Inf. Fusion"},{"key":"11_CR8","doi-asserted-by":"publisher","unstructured":"Ibrahim, O.A., Keller, J.M., Bezdek, J.C.: Evaluating evolving structure in streaming data with modified Dunn\u2019s indices. IEEE Trans. Emerg. Top. Comput. Intell. (2020, in press). https:\/\/doi.org\/10.1109\/TETCI.2019.2909521","DOI":"10.1109\/TETCI.2019.2909521"},{"key":"11_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1007\/11573036_32","volume-title":"Advances in Informatics","author":"I Katakis","year":"2005","unstructured":"Katakis, I., Tsoumakas, G., Vlahavas, I.: On the utility of incremental feature selection for the classification of textual data streams. In: Bozanis, P., Houstis, E.N. (eds.) PCI 2005. LNCS, vol. 3746, pp. 338\u2013348. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11573036_32"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Katakis, I., Tsoumakas, G., Vlahavas, I.: Tracking recurring contexts using ensemble classifiers: an application to email filtering. In: Proceedings of the KAIS (2009)","DOI":"10.1007\/s10115-009-0206-2"},{"key":"11_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/11564126_15","volume-title":"Knowledge Discovery in Databases: PKDD 2005","author":"Y-N Law","year":"2005","unstructured":"Law, Y.-N., Zaniolo, C.: An adaptive nearest neighbor classification algorithm for data streams. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 108\u2013120. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564126_15"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.bushor.2015.03.008","volume":"58","author":"I Lee","year":"2015","unstructured":"Lee, I., Lee, K.: The Internet of Things (IoT): applications, investments, and challenges for enterprises. Bus. Horiz. 58, 431\u2013440 (2015)","journal-title":"Bus. Horiz."},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1002\/int.22064","volume":"34","author":"M Moshtaghi","year":"2019","unstructured":"Moshtaghi, M., Bezdek, J.C., Erfani, S.M., Leckie, C., Bailey, J.: Online cluster validity indices for performance monitoring of streaming data clustering. Int. J. Intell. Syst. 34, 541\u2013563 (2019)","journal-title":"Int. J. Intell. Syst."},{"key":"11_CR14","unstructured":"Otey, M.E., Wang, C., Parthasarathy, S., Veloso, A., Meira, W.: Mining frequent itemsets in distributed and dynamic database. In: Proceedings of the ICDM 2003 (2003)"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Wenerstrom, B., Giraud-Carrier, C.: Temporal data mining in dynamic feature spaces. In: Proceedings of the ICDM 2006 (2006)","DOI":"10.1109\/ICDM.2006.157"},{"key":"11_CR16","unstructured":"Zliobaite, I.: Learning under concept drift: an overview. Arxiv:1010.4784v1 (2010). https:\/\/arxiv.org\/pdf\/1010.4784.pdf"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.eswa.2018.07.049","volume":"115","author":"G Sanghani","year":"2019","unstructured":"Sanghani, G., Kotecha, K.: Incremental personalized E-mail spam filter using novel TFDCR feature selection with dynamic feature update. Expert Syst. Appl. 115, 287\u2013299 (2019)","journal-title":"Expert Syst. Appl."},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.ins.2016.03.043","volume":"357","author":"G Song","year":"2016","unstructured":"Song, G., Ye, Y., Zhang, H., Xu, X., Lau, R.Y.K., Liu, F.: Dynamic clustering forest: an ensemble framework to efficiently classify textual data stream with concept drift. Inf. Sci. 357, 125\u2013143 (2016)","journal-title":"Inf. Sci."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Steinhauer, H.J., Helldin, T., Mathiason, G., Karlsson, A.: Topic modeling for anomaly detection in telecommunication networks. J. Ambient Intell. Humanized Comput. (2019, in press)","DOI":"10.1007\/s12652-019-01372-5"},{"key":"11_CR20","unstructured":"http:\/\/byubookstore.com"},{"key":"11_CR21","unstructured":"http:\/\/www.ppdm.cat\/gransDades.php"},{"key":"11_CR22","unstructured":"http:\/\/spamassassin.apache.org\/publiccorpus\/"},{"key":"11_CR23","unstructured":"https:\/\/towardsdatascience.com\/why-machine-learning-models-degrade-in-production-d0f2108e9214"},{"key":"11_CR24","unstructured":"http:\/\/www.ecn.purdue.edu\/KDDCUP\/"},{"key":"11_CR25","unstructured":"http:\/\/www.ppdm.cat\/links.php"}],"container-title":["Lecture Notes in Computer Science","Integrated Uncertainty in Knowledge Modelling and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62509-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T23:09:17Z","timestamp":1619305757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-62509-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030625085","9783030625092"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62509-2_11","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":"2 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IUKM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Phuket","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","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":"11 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2020","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":"iukm2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.jaist.ac.jp\/IUKM\/IUKM2020\/index.php","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":"http:\/\/easychair.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55","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":"35","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":"64% - 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":"2.24","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)"}},{"value":"The conference was held 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)"}}]}}