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Data"],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            We study on-the-fly classification of evolving text streams in which the relation between the input data and target labels changes over time\u2014i.e., \u201cconcept drift.\u201d These variations decrease the model\u2019s performance, as predictions become less accurate over time and they necessitate a more adaptable system. While most studies focus on concept drift detection and handling with ensemble approaches, the application of neural models in this area is relatively less studied. We introduce Adaptive Neural Ensemble Network (\n            <jats:italic>AdaNEN<\/jats:italic>\n            ), a novel ensemble-based neural approach, capable of handling concept drift in data streams. With our novel architecture, we address some of the problems neural models face when exploited for online adaptive learning environments. Most current studies address concept drift detection and handling in numerical streams, and the evolving text stream classification remains relatively unexplored. We hypothesize that the lack of public and large-scale experimental data could be one reason. To this end, we propose a method based on an existing approach for generating evolving text streams by introducing various types of concept drifts to real-world text datasets. We provide an extensive evaluation of our proposed approach using 12 state-of-the-art baselines and 13 datasets. We first evaluate concept drift handling capability of AdaNEN and the baseline models on evolving numerical streams; this aims to demonstrate the concept drift handling capabilities of our method on a general spectrum and motivate its use in evolving text streams. The models are then evaluated in evolving text stream classification. Our experimental results show that AdaNEN consistently outperforms the existing approaches in terms of predictive performance with conservative efficiency.\n          <\/jats:p>","DOI":"10.1145\/3639054","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T21:57:51Z","timestamp":1703800671000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Neural Ensemble Architecture for On-the-fly Classification of Evolving Text Streams"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3479-8842","authenticated-orcid":false,"given":"Pouya","family":"Ghahramanian","sequence":"first","affiliation":[{"name":"Bilkent University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2292-6130","authenticated-orcid":false,"given":"Sepehr","family":"Bakhshi","sequence":"additional","affiliation":[{"name":"Bilkent University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2811-706X","authenticated-orcid":false,"given":"Hamed","family":"Bonab","sequence":"additional","affiliation":[{"name":"Amazon Inc., USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0016-4278","authenticated-orcid":false,"given":"Fazli","family":"Can","sequence":"additional","affiliation":[{"name":"Bilkent University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,13]]},"reference":[{"issue":"2","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1145\/2641190.2641194","article-title":"Mining text and social streams: A review","volume":"15","author":"Aggarwal Charu C.","year":"2014","unstructured":"Charu C. 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