{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:02:54Z","timestamp":1742932974502,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031255984"},{"type":"electronic","value":"9783031255991"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25599-1_28","type":"book-chapter","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T04:32:27Z","timestamp":1678249947000},"page":"370-383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Neural Network Based Drift Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7487-7110","authenticated-orcid":false,"given":"Christofer","family":"Fellicious","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9459-6244","authenticated-orcid":false,"given":"Lorenz","family":"Wendlinger","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3566-5507","authenticated-orcid":false,"given":"Michael","family":"Granitzer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"28_CR1","unstructured":"Babu, G., Feigelson, E.: Astrostatistics: goodness-of-fit and all that! In: Astronomical Data Analysis Software and Systems XV, vol. 351, p. 127 (2006)"},{"key":"28_CR2","unstructured":"Baena-Garc\u0131a, M., del Campo-\u00c1vila, J., Fidalgo, R., Bifet, A., Gavalda, R., Morales-Bueno, R.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams, vol. 6, pp. 77\u201386 (2006)"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Bayram, F., Ahmed, B.S., Kassler, A.: From concept drift to model degradation: an overview on performance-aware drift detectors. Knowl.-Based Syst. 108632 (2022)","DOI":"10.1016\/j.knosys.2022.108632"},{"key":"28_CR4","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":"28_CR5","unstructured":"Engel, J., Agrawal, K.K., Chen, S., Gulrajani, I., Donahue, C., Roberts, A.: Gansynth: adversarial neural audio synthesis. arXiv preprint arXiv:1902.08710 (2019)"},{"key":"28_CR6","doi-asserted-by":"publisher","unstructured":"Fisher, R.A.: Statistical methods for research workers. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer Series in Statistics, pp. 66\u201370. Springer, New York (1992). https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_6","DOI":"10.1007\/978-1-4612-4380-9_6"},{"key":"28_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"},{"issue":"4","key":"28_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama, J., \u017dliobait\u0117, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Computi. Surv. (CSUR) 46(4), 1\u201337 (2014)","journal-title":"ACM Computi. Surv. (CSUR)"},{"key":"28_CR9","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"G\u00f6z\u00fca\u00e7\u0131k, \u00d6., B\u00fcy\u00fck\u00e7ak\u0131r, A., Bonab, H., Can, F.: Unsupervised concept drift detection with a discriminative classifier. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2365\u20132368 (2019)","DOI":"10.1145\/3357384.3358144"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2255\u20132264 (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"key":"28_CR12","unstructured":"Harries, M., Wales, N.S.: Splice-2 comparative evaluation: electricity pricing (1999)"},{"issue":"2","key":"28_CR13","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1327","volume":"10","author":"H Hu","year":"2020","unstructured":"Hu, H., Kantardzic, M., Sethi, T.S.: No free lunch theorem for concept drift detection in streaming data classification: a review. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 10(2), e1327 (2020)","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Discov."},{"issue":"3","key":"28_CR14","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/S0167-7152(97)00020-5","volume":"35","author":"A Justel","year":"1997","unstructured":"Justel, A., Pe\u00f1a, D., Zamar, R.: A multivariate Kolmogorov-Smirnov test of goodness of fit. Stat. Probab. Lett. 35(3), 251\u2013259 (1997)","journal-title":"Stat. Probab. Lett."},{"key":"28_CR15","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"de Lima Cabral, D.R., de Barros, R.S.M.: Concept drift detection based on fisher\u2019s exact test. Inf. Sci. 442, 220\u2013234 (2018)","DOI":"10.1016\/j.ins.2018.02.054"},{"key":"28_CR17","first-page":"469","volume":"29","author":"MY Liu","year":"2016","unstructured":"Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. Adv. Neural. Inf. Process. Syst. 29, 469\u2013477 (2016)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Losing, V., Hammer, B., Wersing, H.: KNN classifier with self adjusting memory for heterogeneous concept drift. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 291\u2013300. IEEE (2016)","DOI":"10.1109\/ICDM.2016.0040"},{"issue":"1","key":"28_CR19","first-page":"2914","volume":"19","author":"J Montiel","year":"2018","unstructured":"Montiel, J., Read, J., Bifet, A., Abdessalem, T.: Scikit-multiflow: a multi-output streaming framework. J. Mach. Learn. Res. 19(1), 2914\u20132915 (2018)","journal-title":"J. Mach. Learn. Res."},{"key":"28_CR20","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"dos Reis, D.M., Flach, P., Matwin, S., Batista, G.: Fast unsupervised online drift detection using incremental Kolmogorov-Smirnov test. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1545\u20131554 (2016)","DOI":"10.1145\/2939672.2939836"},{"issue":"1","key":"28_CR22","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Suprem, A., Arulraj, J., Pu, C., Ferreira, J.: Odin: automated drift detection and recovery in video analytics. arXiv preprint arXiv:2009.05440 (2020)","DOI":"10.14778\/3407790.3407837"},{"key":"28_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-11021-5_5","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"X Wang","year":"2019","unstructured":"Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63\u201379. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11021-5_5"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Zhan, F., Zhu, H., Lu, S.: Spatial fusion GAN for image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3653\u20133662 (2019)","DOI":"10.1109\/CVPR.2019.00377"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25599-1_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T17:15:29Z","timestamp":1680714929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25599-1_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031255984","9783031255991"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25599-1_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Certosa di Pontignano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","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":"lod2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2022.icas.cc\/","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":"226","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":"85","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":"38% - 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":"5.6","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":"1.5","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)"}}]}}