{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:04:40Z","timestamp":1743087880334,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031407246"},{"type":"electronic","value":"9783031407253"}],"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\/978-3-031-40725-3_8","type":"book-chapter","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T23:02:46Z","timestamp":1693263766000},"page":"84-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Fuzzy Logic Ensemble Approach to\u00a0Concept Drift Detection"],"prefix":"10.1007","author":[{"given":"Carlos","family":"del Campo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Borja","family":"Sanz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jon","family":"D\u00edaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique","family":"Onieva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Amador Coelho, R., Bambirra Torres, L.C., Leite de Castro, C.: Concept drift detection with quadtree-based spatial mapping of streaming data. Inf. Sci. 625, 578\u2013592 (2023). https:\/\/doi.org\/10.1016\/j.ins.2022.12.085, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025522015808","key":"8_CR1","DOI":"10.1016\/j.ins.2022.12.085"},{"doi-asserted-by":"publisher","unstructured":"Bibinbe, A.M.S.N., Mahamadou, A.J., Mbouopda, M.F., Nguifo, E.M.: DragStream: an anomaly and concept drift detector in univariate data streams. In: 2022 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 842\u2013851 (2022). https:\/\/doi.org\/10.1109\/ICDMW58026.2022.00113","key":"8_CR2","DOI":"10.1109\/ICDMW58026.2022.00113"},{"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)","key":"8_CR3","DOI":"10.1007\/s10994-022-06188-7"},{"unstructured":"Choudhary, V., Gupta, B., Chatterjee, A., Paul, S., Banerjee, K., Agneeswaran, V.: Detecting concept drift in the presence of sparsity-a case study of automated change risk assessment system. arXiv preprint arXiv:2207.13287 (2022)","key":"8_CR4"},{"key":"8_CR5","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/978-981-16-3346-1_13","volume-title":"Proceedings of Second Doctoral Symposium on Computational Intelligence","author":"KS Desale","year":"2022","unstructured":"Desale, K.S., Shinde, S.V.: Addressing concept drifts using deep learning for heart disease prediction: a review. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds.) Proceedings of Second Doctoral Symposium on Computational Intelligence. AISC, vol. 1374, pp. 157\u2013167. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-3346-1_13"},{"unstructured":"Green, D.H., Langham, A.W., Agustin, R.A., Quinn, D.W., Leeb, S.B.: Adaptation for automated drift detection in electromechanical machine monitoring. IEEE Trans. Neural Netw. Learn. Syst. 1\u201315(2022)","key":"8_CR6"},{"doi-asserted-by":"publisher","unstructured":"Grulich, P., Saitenmacher, R., Traub, J., BreSS, S., Rabl, T., Markl, V.: Scalable detection of concept drifts on data streams with parallel adaptive windowing (2018). https:\/\/doi.org\/10.5441\/002\/edbt.2018.51","key":"8_CR7","DOI":"10.5441\/002\/edbt.2018.51"},{"doi-asserted-by":"publisher","unstructured":"Klikowski, J.: Concept drift detector based on centroid distance analysis. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2022). https:\/\/doi.org\/10.1109\/IJCNN55064.2022.9892399","key":"8_CR8","DOI":"10.1109\/IJCNN55064.2022.9892399"},{"doi-asserted-by":"publisher","unstructured":"Komorniczak, J., Zyblewski, P., Ksieniewicz, P.: Statistical drift detection ensemble for batch processing of data streams. Knowl. Based Syst. 252, 109380 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109380, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S095070512200692X","key":"8_CR9","DOI":"10.1016\/j.knosys.2022.109380"},{"key":"8_CR10","doi-asserted-by":"publisher","first-page":"116206","DOI":"10.1016\/j.eswa.2021.116206","volume":"190","author":"S Lee","year":"2022","unstructured":"Lee, S., Park, S.H.: Concept drift modeling for robust autonomous vehicle control systems in time-varying traffic environments. Expert Syst. Appl. 190, 116206 (2022)","journal-title":"Expert Syst. Appl."},{"doi-asserted-by":"publisher","unstructured":"Lima, M., Neto, M., Filho, T.S., de A. Fagundes, R.A.: Learning under concept drift for regression\u2013a systematic literature review. IEEE Access 10, 45410\u201345429 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3169785","key":"8_CR11","DOI":"10.1109\/ACCESS.2022.3169785"},{"issue":"6","key":"8_CR12","doi-asserted-by":"publisher","first-page":"3198","DOI":"10.1109\/TCYB.2020.2983962","volume":"51","author":"A Liu","year":"2021","unstructured":"Liu, A., Lu, J., Zhang, G.: Concept drift detection via equal intensity k-means space partitioning. IEEE Trans. Cybern. 51(6), 3198\u20133211 (2021). https:\/\/doi.org\/10.1109\/TCYB.2020.2983962","journal-title":"IEEE Trans. Cybern."},{"doi-asserted-by":"publisher","unstructured":"L\u00f3pez Lobo, J.: Synthetic datasets for concept drift detection purposes (2020). https:\/\/doi.org\/10.7910\/DVN\/5OWRGB","key":"8_CR13","DOI":"10.7910\/DVN\/5OWRGB"},{"doi-asserted-by":"publisher","unstructured":"Mavromatis, I., et al.: Le3d: A lightweight ensemble framework of data drift detectors for resource-constrained devices (2022). https:\/\/doi.org\/10.48550\/ARXIV.2211.01840 , https:\/\/arxiv.org\/abs\/2211.01840","key":"8_CR14","DOI":"10.48550\/ARXIV.2211.01840"},{"doi-asserted-by":"publisher","unstructured":"Mouss, H., Mouss, M., Mouss, K., Linda, S.: Test of page-hinckley, an approach for fault detection in an agro-alimentary production system, vol. 2, pp. 815\u2013818 (2004). DOI: https:\/\/doi.org\/10.1109\/ASCC.2004.184970","key":"8_CR15","DOI":"10.1109\/ASCC.2004.184970"},{"doi-asserted-by":"publisher","unstructured":"Poenaru-Olaru, L., Cruz, L., van Deursen, A., Rellermeyer, J.S.: Are concept drift detectors reliable alarming systems? - a comparative study (2022). https:\/\/doi.org\/10.48550\/ARXIV.2211.13098, https:\/\/arxiv.org\/abs\/2211.13098","key":"8_CR16","DOI":"10.48550\/ARXIV.2211.13098"},{"issue":"4","key":"8_CR17","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1109\/TSMCA.2012.2224338","volume":"43","author":"J Sun","year":"2013","unstructured":"Sun, J., Li, H., Adeli, H.: Concept drift-oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction. IEEE Trans. Syst. Man, Cybern. Syst. 43(4), 801\u2013813 (2013)","journal-title":"IEEE Trans. Syst. Man, Cybern. Syst."},{"doi-asserted-by":"publisher","unstructured":"Togbe, M.U., Chabchoub, Y., Boly, A., Barry, M., Chiky, R., Bahri, M.: Anomalies detection using isolation in concept-drifting data streams. Computers 10(1), 13 (2021). https:\/\/doi.org\/10.3390\/computers10010013, https:\/\/www.mdpi.com\/2073-431X\/10\/1\/13","key":"8_CR18","DOI":"10.3390\/computers10010013"},{"doi-asserted-by":"publisher","unstructured":"Yu, H., Zhang, Q., Liu, T., Lu, J., Wen, Y., Zhang, G.: Meta-add: a meta-learning based pre-trained model for concept drift active detection. Inf. Sci. 608, 996\u20131009 (2022). https:\/\/doi.org\/10.1016\/j.ins.2022.07.022, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025522007125","key":"8_CR19","DOI":"10.1016\/j.ins.2022.07.022"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40725-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T06:03:32Z","timestamp":1715148212000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40725-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031407246","9783031407253"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40725-3_8","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":"29 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 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":"hais2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2023.haisconference.eu\/","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":"120","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":"65","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":"54% - 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":"2","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)"}}]}}