Classification Report for Traditional Classification Models. by Jacqueline Jia Hsin Hu (22783501)

Subjects:

Classification model parameter settings. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

Comparison of classification models. by Mark Mets (18452224)

Subjects:

Accuracies of the classification models. by Semyon K. Kolmykov (7326089)

“…<p>Accuracies of the classification models.</p>…”

Models’ classification report. by Muhammad Muzzammil Auzine (18880720)

Best classification models. by Marie Arsalidou (8053202)

Subjects:

The performance of the classification models. by Yuki Shimizu (2545252)

Classification results of models. by Xu Yang (112496)

“…Firstly, hyperspectral images of seven varieties of soybean, H1, H2, H3, H4, H5, H6 and H7, were collected by hyperspectral imager, and by using the principle of the three base colours, the R, G and B bands which have more characteristic information are selected to reconstruct the images with different texture and colour characteristics to generate a new dataset for seed segmentation, and finally, a comparison is made with the classification effect of the seven models. The experimental results in ResNet34 show that the classification accuracy of the dataset before and after RGB reconstruction increases from 88.87% to 91.75%, demonstrating that RGB image reconstruction can strengthen image features; ResNet18, ResNet34, ResNet50, ResNet101, CBAM-ResNet34, SENet-ResNet34, and SENet-ResNet34-DCN models have classification accuracies of 72.25%, 91.75%, 89%, 88.48%, 92.28%, 92.80%, and 94.24%, respectively.SENet-ResNet34-DCN achieves the greatest classification accuracy results, with a model loss of roughly 0.3. …”

The details of the classification models. by Ramtin Zargari Marandi (592550)

“…<p>The details of the classification models.</p>…”

Comparison of classification models. by David F. Read (7373165)

“…<p>(A) Classification models outperform all individual features for use in classification of gene expression. …”

Classification results of models. by Xu Yang (112496)

“…Firstly, hyperspectral images of seven varieties of soybean, H1, H2, H3, H4, H5, H6 and H7, were collected by hyperspectral imager, and by using the principle of the three base colours, the R, G and B bands which have more characteristic information are selected to reconstruct the images with different texture and colour characteristics to generate a new dataset for seed segmentation, and finally, a comparison is made with the classification effect of the seven models. The experimental results in ResNet34 show that the classification accuracy of the dataset before and after RGB reconstruction increases from 88.87% to 91.75%, demonstrating that RGB image reconstruction can strengthen image features; ResNet18, ResNet34, ResNet50, ResNet101, CBAM-ResNet34, SENet-ResNet34, and SENet-ResNet34-DCN models have classification accuracies of 72.25%, 91.75%, 89%, 88.48%, 92.28%, 92.80%, and 94.24%, respectively.SENet-ResNet34-DCN achieves the greatest classification accuracy results, with a model loss of roughly 0.3. …”

ROC curve for the proposed Classification model across AD, MCI, and CN. by Muhammad Zubair (728141)

Subjects:

Comparison of Model Five-classification Results. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

The classification reports for experiments with tweet-level classification models. by Ercan Canhasi (17457899)

“…<p>The classification reports for experiments with tweet-level classification models.…”

Probability of correct classification for classification models with different SNRs. by Haoran Yin (5793305)

Subjects:

The classification of model variables. by Zhenkai Ma (5813405)

“…<p>The classification of model variables.</p>…”

TME model classification. by Linyu Peng (10701375)

Evaluation of classification model. by Handi Sun (15332670)

“…The experimental results demonstrate that our proposed model outperforms state-of-the-art methods on imbalanced datasets.…”

Classification tree model. by Young-Soo Chang (3219999)

Issue Classification Model by Markus Neumann (19757260)

“…<p dir="ltr"><b>Cross-platform Election Advertising Transparency Initiative (CREATIVE):</b> issue classification model that predicts which political issues (e.g., taxes, crime, abortion, etc.; out of 65 potential issue categories) are mentioned in ad. …”