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Methods for Generating Drift in Text Streams
March 20, 2024, 4:41 a.m. | Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto Jr, Jean Paul Barddal
cs.LG updates on arXiv.org arxiv.org
Abstract: Systems and individuals produce data continuously. On the Internet, people share their knowledge, sentiments, and opinions, provide reviews about services and products, and so on. Automatically learning from these textual data can provide insights to organizations and institutions, thus preventing financial impacts, for example. To learn from textual data over time, the machine learning system must account for concept drift. Concept drift is a frequent phenomenon in real-world datasets and corresponds to changes in data …
abstract arxiv cs.cl cs.ir cs.lg data drift example financial impacts insights internet knowledge learn opinions organizations people products reviews services systems text textual type
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