{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:09:58Z","timestamp":1768414198238,"version":"3.49.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Data Mining in non-stationary data streams is particularly relevant  in the context of the Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self-Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm. SAM-kNN can deal with heterogeneous concept drift, i.e. different drift types and rates. Its basic idea are dedicated models for current and former concepts used according to the demands of the given situation. It can be robustly applied in practice without meta parameter optimization.\n\nWe conduct an extensive evaluation on various benchmarks, consisting of artificial streams with known drift characteristics and real-world datasets. Highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capability to handle heterogeneous concept drift.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/690","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"4899-4903","source":"Crossref","is-referenced-by-count":18,"title":["Self-Adjusting Memory: How to Deal with Diverse Drift Types"],"prefix":"10.24963","author":[{"given":"Viktor","family":"Losing","sequence":"first","affiliation":[{"name":"Bielefeld University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Hammer","sequence":"additional","affiliation":[{"name":"Bielefeld University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heiko","family":"Wersing","sequence":"additional","affiliation":[{"name":"HONDA Research Institute Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:55:08Z","timestamp":1501242908000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/690"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/690","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}