Fine-Tuned Class Weights for Improved Classification Accuracy.

<p>Fine-Tuned Class Weights for Improved Classification Accuracy.</p>

Αποθηκεύτηκε σε:

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας:	Rahman Farnoosh (22339496) (author)
Άλλοι συγγραφείς:	Karlo Abnoosian (22339499) (author), Rasha Abbas Isewid (22339502) (author), Danial Javaheri (18258222) (author)
Έκδοση:	2025
Θέματα:	Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified shows meaningful gains precision (+ 2 limited predictive performance healthcare systems worldwide enhancing model fairness demonstrates significant improvements deep learning models auc (+ 4 98 %, auc 95 %, precision 08 %, recall precision (+ 5 improve dataset quality germany diabetes datasets generalized attention mechanisms aware feature enhancer auc (+ 0 tabular medical data score (+ 1 study introduces diabetesxpertnet score (+ 5 including mean imputation 8 %), f1 1 %), f1 0 %), 11 %, 5 %). pid dataset innovative attention imbalanced datasets feature selection 9 %), image data dimensional data specifically tailored severe complications results underscore promising tool mutual information missing values median replacement logistic regression lasso regression insulin levels frankfurt hospital diabetesxpertnet achieves computational efficiency based cnn 6 %).
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