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Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Data streams typically evolve over time, manifesting concept drift, and are applied to Deep Neural Networks (DNNs). However, DNNs are predominantly not designed to adapt, resulting in decreasing accuracy. Ideally, a DNN would detect this drift and adapt rapidly. In the streaming machine learning field, adapting models to concept drift has been the subject of research for some time. However, DNN adaptation is lagging behind as DNNs typically require a greater number of instances and increased adaptation time compared to other model types, resulting in a lack of methods that adapt the classifying DNN in a streaming environment. The focus of this study is detection and adaptation to concept drift in high dimensional data with an experimental study on images where novel sub-classes are arising. In the concept drift field, less attention has been focused upon high dimensional data. Our system, termed DeepStreamEnsemble provides existing DNNs with concept drift detection and DNN adaptation capabilities. It is the first system to utilise DNN activations and streaming classifiers for concept drift detection and adaptation. We compare with eleven other state-of-the-art methods, with DeepStreamEnsemble outperforming other leading concept drift detection and adaptation methods by between 5% and 20% on accuracy.<\/jats:p>","DOI":"10.1007\/s13042-024-02492-x","type":"journal-article","created":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T06:04:42Z","timestamp":1735970682000},"page":"3955-3976","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deepstreamensemble: streaming adaptation to concept drift in deep neural networks"],"prefix":"10.1007","volume":"16","author":[{"given":"Lorraine","family":"Chambers","sequence":"first","affiliation":[]},{"given":"Mohamed-Medhat","family":"Gaber","sequence":"additional","affiliation":[]},{"given":"Hossein","family":"Ghomeshi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,4]]},"reference":[{"key":"2492_CR1","doi-asserted-by":"crossref","unstructured":"Aljundi R, Belilovsky E, Tuytelaars T, Charlin L, Caccia M, Lin M, Page-Caccia L (2019) Online continual learning with maximal interfered retrieval. 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