{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:56:41Z","timestamp":1781650601557,"version":"3.54.5"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819998920","type":"print"},{"value":"9789819998937","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-9893-7_15","type":"book-chapter","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T18:03:08Z","timestamp":1705946588000},"page":"192-208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Unsupervised Concept Drift Detection Based on\u00a0Stacked Autoencoder and\u00a0Page-Hinckley Test"],"prefix":"10.1007","author":[{"given":"Shu","family":"Zhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunlong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Agrahari, S., Singh, A.K.: Concept drift detection in data stream mining: a literature review. J. King Saud Univ.-Comput. Inform. Sci. (2021)","DOI":"10.1016\/j.jksuci.2021.11.006"},{"key":"15_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1007\/978-3-642-28931-6_50","volume-title":"Hybrid Artificial Intelligent Systems","author":"Z Ahmadi","year":"2012","unstructured":"Ahmadi, Z., Beigy, H.: Semi-supervised ensemble learning of data streams in the presence of concept drift. In: Corchado, E., Sn\u00e1\u0161el, V., Abraham, A., Wo\u017aniak, M., Gra\u00f1a, M., Cho, S.-B. (eds.) HAIS 2012. LNCS (LNAI), vol. 7209, pp. 526\u2013537. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28931-6_50"},{"key":"15_CR3","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":"15_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":"15_CR5","unstructured":"Bifet, A., et al.: Moa: Massive online analysis, a framework for stream classification and clustering. In: Proceedings of the First Workshop on Applications of Pattern Analysis, pp. 44\u201350. PMLR (2010)"},{"key":"15_CR6","unstructured":"Bodik, P., Hong, W., Guestrin, C., Madden, S., Paskin, M., Thibaux, R.: MIT sensor data. http:\/\/db.csail.mit.edu\/labdata\/labdata.html (2004)"},{"issue":"2","key":"15_CR7","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1109\/TNNLS.2016.2619909","volume":"29","author":"L Bu","year":"2016","unstructured":"Bu, L., Alippi, C., Zhao, D.: A pdf-free change detection test based on density difference estimation. IEEE Trans. Neural Netw. Learn. Syst. 29(2), 324\u2013334 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"10","key":"15_CR8","doi-asserted-by":"publisher","first-page":"2714","DOI":"10.1109\/TSMC.2017.2682502","volume":"47","author":"L Bu","year":"2017","unstructured":"Bu, L., Zhao, D., Alippi, C.: An incremental change detection test based on density difference estimation. IEEE Trans. Syst. Man Cybern. Syst. 47(10), 2714\u20132726 (2017)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Cerqueira, V., Gomes, H.M., Bifet, A., Torgo, L.: Studd: a student-teacher method for unsupervised concept drift detection. Mach. Learn. 1\u201328 (2022)","DOI":"10.1007\/s10994-022-06188-7"},{"key":"15_CR10","unstructured":"Dasu, T., Krishnan, S., Venkatasubramanian, S., Yi, K.: An information-theoretic approach to detecting changes in multi-dimensional data streams. In: Proceedings of Symposium on the Interface of Statistics, Computing Science, and Applications (Interface) (2006)"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Ditzler, G., Polikar, R.: Hellinger distance based drift detection for nonstationary environments. In: 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 41\u201348. IEEE (2011)","DOI":"10.1109\/CIDUE.2011.5948491"},{"issue":"10","key":"15_CR12","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/TNN.2011.2160459","volume":"22","author":"R Elwell","year":"2011","unstructured":"Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517\u20131531 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"3","key":"15_CR13","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1109\/TKDE.2014.2345382","volume":"27","author":"I Frias-Blanco","year":"2014","unstructured":"Frias-Blanco, I., del Campo-\u00c1vila, J., Ramos-Jimenez, G., Morales-Bueno, R., Ortiz-Diaz, A., Caballero-Mota, Y.: Online and non-parametric drift detection methods based on hoeffding\u2019s bounds. IEEE Trans. Knowl. Data Eng. 27(3), 810\u2013823 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR14","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":"15_CR15","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":"15_CR16","doi-asserted-by":"crossref","unstructured":"Haque, A., Khan, L., Baron, M.: Sand: semi-supervised adaptive novel class detection and classification over data stream. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)","DOI":"10.1609\/aaai.v30i1.10283"},{"key":"15_CR17","unstructured":"Hopkins, M., Reeber, E., Forman, G., Suermondt, J.: UCI Machine Learning Repository - Spambase Dataset (1999). http:\/\/archive.ics.uci.edu\/ml\/datasets\/Spambase"},{"issue":"3","key":"15_CR18","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/s10115-015-0837-4","volume":"46","author":"MJ Hosseini","year":"2016","unstructured":"Hosseini, M.J., Gholipour, A., Beigy, H.: An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams. Knowl. Inf. Syst. 46(3), 567\u2013597 (2016)","journal-title":"Knowl. Inf. Syst."},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: VLDB, Toronto, Canada, vol. 4, pp. 180\u2013191 (2004)","DOI":"10.1016\/B978-012088469-8.50019-X"},{"key":"15_CR20","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.patcog.2017.11.009","volume":"76","author":"A Liu","year":"2018","unstructured":"Liu, A., Lu, J., Liu, F., Zhang, G.: Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recogn. 76, 256\u2013272 (2018)","journal-title":"Pattern Recogn."},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Liu, A., Song, Y., Zhang, G., Lu, J.: Regional concept drift detection and density synchronized drift adaptation. In: IJCAI International Joint Conference on Artificial Intelligence (2017)","DOI":"10.24963\/ijcai.2017\/317"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Losing, V., Hammer, B., Wersing, H.: Interactive online learning for obstacle classification on a mobile robot. In: 2015 International Joint Conference on Neural Networks (ijcnn), pp. 1\u20138. IEEE (2015)","DOI":"10.1109\/IJCNN.2015.7280610"},{"key":"15_CR23","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":"12","key":"15_CR24","first-page":"2346","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: A review. IEEE Trans. Knowl. Data Eng. 31(12), 2346\u20132363 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR25","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.artint.2014.01.001","volume":"209","author":"N Lu","year":"2014","unstructured":"Lu, N., Zhang, G., Lu, J.: Concept drift detection via competence models. Artif. Intell. 209, 11\u201328 (2014)","journal-title":"Artif. Intell."},{"issue":"5","key":"15_CR26","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1109\/TKDE.2009.156","volume":"22","author":"LL Minku","year":"2009","unstructured":"Minku, L.L., White, A.P., Yao, X.: The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans. Knowl. Data Eng. 22(5), 730\u2013742 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Qahtan, A.A., Alharbi, B., Wang, S., Zhang, X.: A pca-based change detection framework for multidimensional data streams: change detection in multidimensional data streams. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 935\u2013944 (2015)","DOI":"10.1145\/2783258.2783359"},{"key":"15_CR28","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neucom.2019.11.111","volume":"416","author":"C Raab","year":"2020","unstructured":"Raab, C., Heusinger, M., Schleif, F.M.: Reactive soft prototype computing for concept drift streams. Neurocomputing 416, 340\u2013351 (2020)","journal-title":"Neurocomputing"},{"issue":"4","key":"15_CR29","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1198\/TECH.2011.10069","volume":"53","author":"GJ Ross","year":"2011","unstructured":"Ross, G.J., Tasoulis, D.K., Adams, N.M.: Nonparametric monitoring of data streams for changes in location and scale. Technometrics 53(4), 379\u2013389 (2011)","journal-title":"Technometrics"},{"key":"15_CR30","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.eswa.2017.04.008","volume":"82","author":"TS Sethi","year":"2017","unstructured":"Sethi, T.S., Kantardzic, M.: On the reliable detection of concept drift from streaming unlabeled data. Expert Syst. Appl. 82, 77\u201399 (2017)","journal-title":"Expert Syst. Appl."},{"key":"15_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/978-3-642-23857-4_12","volume-title":"Adaptive and Intelligent Systems","author":"P Sobhani","year":"2011","unstructured":"Sobhani, P., Beigy, H.: New drift detection method for data streams. In: Bouchachia, A. (ed.) ICAIS 2011. LNCS (LNAI), vol. 6943, pp. 88\u201397. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23857-4_12"},{"key":"15_CR32","first-page":"1","volume":"57","author":"RJ Tibshirani","year":"1993","unstructured":"Tibshirani, R.J., Efron, B.: An introduction to the bootstrap. Monographs Stat. Appli. Probabil. 57, 1\u2013436 (1993)","journal-title":"Monographs Stat. Appli. Probabil."}],"container-title":["Lecture Notes in Computer Science","Green, Pervasive, and Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9893-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T18:05:36Z","timestamp":1705946736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9893-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819998920","9789819998937"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9893-7_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GPC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Green, Pervasive, and Cloud Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gpc2023","order":10,"name":"conference_id","label":"Conference ID","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"111","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":"1","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":"34% - 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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}