Multi-Label Classification in Text Data: An Examination on Innovative Technologies
Author:
Affiliation:
1. Firat University,Department of Software Engineering,Elazig,Turkey
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10526462/10526466/10527341.pdf?arnumber=10527341
Reference20 articles.
1. Informatization of innovative technologies for ensuring macroeconomic trends in the conditions of a circular economy
2. Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types
3. MCICT: Graph convolutional network-based end-to-end model for multi-label classification of imbalanced clinical text
4. Clinical text classification research trends: Systematic literature review and open issues
5. GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification
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