Large-Scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image ClassificationA computer-based automated method of performing classification includes learning a deep neural network by maximizing an area under a receiver operating characteristic curve (AUC) or ...
Particularly, we observed that the ensemble-based method by integrating the predictions of multiple algorithms can give more reliable and robust prediction than the single algorithm, with the AUC score improved to 92.26%. We applied our combinatorial prioritization algorithm to lung neoplasms and breast...
We found that the model is robust to hyperparameter settings within reasonable ranges, and the effect of hyperparameter adjustment on performance is similar across datasets. A potential risk of choosing NB over ZINB is that the model may underfit data generated by plate-based protocols like Smart...
Active diagnosis via AUC maximization: An efficient approach for multiple fault identification in large scale, noisy networks - Bellala, Stanley, et al. - 2011 () Citation Context ...erion under the single fault approximation. Further, in the scenario where only one fault is present, we note...
However, the integration of RumorLLM for data augmentation resulted in significant improvements in F1 score and AUC-ROC values. In particular, the precision of the proposed model is 0.8519, outperforming other models, including LR-Bias (0.7647). In addition, the proposed model achieves a robust ...