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pro vyhledávání: '"Umesh, Chaithra"'
Dependencies among attributes are a common aspect of tabular data. However, whether existing tabular data generation algorithms preserve these dependencies while generating synthetic data is yet to be explored. In addition to the existing notion of f
Externí odkaz:
http://arxiv.org/abs/2409.17684
Generating synthetic samples from the convex space of the minority class is a popular oversampling approach for imbalanced classification problems. Recently, deep-learning approaches have been successfully applied to modeling the convex space of mino
Externí odkaz:
http://arxiv.org/abs/2407.09789
Autor:
Bej, Saptarshi, Umesh, Chaithra, Mahendra, Manjunath, Schultz, Kristian, Sarkar, Jit, Wolkenhauer, Olaf
Publikováno v:
In Machine Learning with Applications 15 December 2023 14
Autor:
Umesh, Chaithra1 (AUTHOR) chaithra.umesh@uni-rostock.de, Mahendra, Manjunath1 (AUTHOR) manjunath.mahendra@uni-rostock.de, Bej, Saptarshi (AUTHOR), Wolkenhauer, Olaf1,2 (AUTHOR), Wolfien, Markus3,4 (AUTHOR)
Publikováno v:
Pflügers Archiv: European Journal of Physiology. Oct2024, p1-12.