{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:26:14Z","timestamp":1730265974557,"version":"3.28.0"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533334","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["Concept Drift Detection via Boundary Shrinking"],"prefix":"10.1109","author":[{"given":"Yoshihiro","family":"Okawa","sequence":"first","affiliation":[]},{"given":"Kenichi","family":"Kobayashi","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207555"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206756"},{"key":"ref12","first-page":"1652","article-title":"Sand: Semi-supervised adaptive novel class detection and classification over data stream","author":"haque","year":"0","journal-title":"30th AAAI Conference on Artificial Intelligence"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206792"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2019.00047"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.70"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2019.01.043"},{"key":"ref17","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"0","journal-title":"Proceedings of the 34th International Conference on Machine Learning (ICML) org"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1002\/sam.10054"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974010.98"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2876857"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.04.008"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.11591\/ijece.v8i1.pp19-25"},{"key":"ref5","first-page":"1394","article-title":"Failing loudly: an empirical study of methods for detecting dataset shift","author":"rabanser","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref8","first-page":"148","article-title":"Experiments with a new boosting algorithm","author":"freund","year":"0","journal-title":"Proc of the International Conference on Machine Learning (ICML)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/BF00058655"},{"key":"ref2","first-page":"13969","article-title":"Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift","author":"snoek","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref9","first-page":"1","article-title":"Randomizing the self-adjusting memory for enhanced handling of concept drift","author":"losing","year":"0","journal-title":"2020 International Joint Conference on Neural Networks (IJCNN)"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CIDUE.2011.5948491"},{"key":"ref22","first-page":"77","article-title":"Early drift detection method","volume":"6","author":"baena-garcia","year":"0","journal-title":"Fourth International Workshop on Knowledge Discovery from Data Streams"},{"key":"ref21","first-page":"286","article-title":"Learning with drift detection","author":"gama","year":"0","journal-title":"Brazilian Symposium on Artificial Intelligence"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2248094"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.42"},{"key":"ref25","first-page":"935","article-title":"A pea-based change detection framework for multidimensional data streams: Change detection in multidimensional data streams","author":"qahtan","year":"0","journal-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533334.pdf?arnumber=9533334","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:46:12Z","timestamp":1652197572000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533334\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533334","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}