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Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification
April 25, 2024, 7:42 p.m. | Pawe{\l} Zyblewski
cs.LG updates on arXiv.org arxiv.org
Abstract: Rapid technological advances are inherently linked to the increased amount of data, a substantial portion of which can be interpreted as data stream, capable of exhibiting the phenomenon of concept drift and having a high imbalance ratio. Consequently, developing new approaches to classifying difficult data streams is a rapidly growing research area. At the same time, the proliferation of deep learning and transfer learning, as well as the success of convolutional neural networks in computer …
abstract advances arxiv classification concept cs.lg data data stream drift embedding interpreted tabular tabular data type word word embedding
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