Search Results for author:
Found 6 papers, 3 papers with code
Structuring the Processing Frameworks for Data Stream Evaluation and Application
The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications.
Cross-Modality Clustering-based Self-Labeling for Multimodal Data Classification
Technological advances facilitate the ability to acquire multimodal data, posing a challenge for recognition systems while also providing an opportunity to use the heterogeneous nature of the information to increase the generalization capability of models.
Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain
Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area.
Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification
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.
Lifelong Learning Natural Language Processing Approach for Multilingual Data Classification
The abundance of information in digital media, which in today's world is the main source of knowledge about current events for the masses, makes it possible to spread disinformation on a larger scale than ever before.
stream-learn -- open-source Python library for difficult data stream batch analysis
stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis.
