Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification

arXiv:2404.15836v1 Announce Type: new
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