Time adjusting dynamic systems whose underlying changing distribution should be continuously
\nmonitored to track abnormal behaviors is one of the most recent challenges in many real life
\napplications. In fields such as sensor networks, intrusion detection, credit card fraud detection and
\nprocess monitoring, the arriving data change over time and the target concept to be learned changes
\naccordingly causing the problem of \u201cconcept drift. Adaptive control charts from SPC domain and
\ndynamic ensemble methods from data mining field are the most widely used techniques to track
\nconcept drift. In order to perform the change identification in data stream processes, the first
\nenhancement of ensemble methods in SPC proposed in Mejri et al, 2018 entitled Dynamic Weighted
\nMajority Control Chart will be presented. The new adaptive method has not only the ability to
\ncombine more than two control charts but also uses the expertise of dynamic weighted majorityWinnow (DWM)-WIN in
\nidentifying and learning changes during the monitoring. First, it transforms
\nthe task of determining the state of the process into a classification problem by treating control charts
\nas attributes where the drift has to be predicted. Second, DWM-WIN mechanism is applied to learn the
\nshift and to combine the decision of different individuals. Third, a prediction of class label is used to
\nhelp in classifying the shift during the changing of the process toward the approximated right
\ndirection. The three main steps of this combined control chart as well comparative results will be
\npresented and discussed in this talk<\/p>\n","protected":false},"excerpt":{"rendered":"
Adaptive Control charts for identifying concept drift in nonstationary environment Time adjusting dynamic systems whose underlying changing distribution should be continuously monitored to track abnormal behaviors is one of the […]<\/p>\n","protected":false},"author":335,"featured_media":0,"template":"","meta":{"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[44],"class_list":["post-738","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-kolloquium-de","cat_kolloquium-de"],"_links":{"self":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/738","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/users\/335"}],"version-history":[{"count":1,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/738\/revisions"}],"predecessor-version":[{"id":739,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/738\/revisions\/739"}],"wp:attachment":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/media?parent=738"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tags?post=738"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events_cat?post=738"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}