The the p-values are for the pairwise comparison of the accuracy of the proposed model to each of the other classifiers. The proposed network had a highest accuracy and the results were significant at the 95% confidence level (indicated by a†).</p
a<p>The number of clusters in the network is determined automatically by the algorithms.</p>b<p>The ...
The scores for the precision, recall, F1-score, and accuracy metrics are shown for all four classifi...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>a) Comparison of the accuracy obtained by the proposed method (left side) and the classical netwo...
<p>The precision-recall curve (A), receiver operating characteristic curve (B), the significance lev...
Performance comparison of Bayesian network classifiers using validation dataset.</p
<p>The sensitivity, specificity and accuracy of each of three classifiers (Linear SVM, RBF SVM, NN) ...
<p>Pr1Rec, Pr10Rec, Pr50Rec, Pr80Rec represent precision at 1%, 10%, 50%, and 80% recall. The last r...
MSBFAN/s: MSBFAN trained/tested with s slices. Note: MSBFAN/1 equivalent MSBPN (n = 1, m = 2).</p
<p>The <i>testMSEs</i> comparisons of prediction performance for four networks.</p
Comparison of the classification performance by the proposed network and other methods.</p
<p>This indicates the extent that each metric can be used to predict performance.</p
Comparison results of different network models: A is training accuracy of model, B is validation acc...
F-beta was calculated as a measure of classification accuracy. See S1 Table in S1 File for optimal h...
Performance metrics of the method as a function of the detection range (m) for the PLN-ARIS dataset:...
a<p>The number of clusters in the network is determined automatically by the algorithms.</p>b<p>The ...
The scores for the precision, recall, F1-score, and accuracy metrics are shown for all four classifi...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...
<p>a) Comparison of the accuracy obtained by the proposed method (left side) and the classical netwo...
<p>The precision-recall curve (A), receiver operating characteristic curve (B), the significance lev...
Performance comparison of Bayesian network classifiers using validation dataset.</p
<p>The sensitivity, specificity and accuracy of each of three classifiers (Linear SVM, RBF SVM, NN) ...
<p>Pr1Rec, Pr10Rec, Pr50Rec, Pr80Rec represent precision at 1%, 10%, 50%, and 80% recall. The last r...
MSBFAN/s: MSBFAN trained/tested with s slices. Note: MSBFAN/1 equivalent MSBPN (n = 1, m = 2).</p
<p>The <i>testMSEs</i> comparisons of prediction performance for four networks.</p
Comparison of the classification performance by the proposed network and other methods.</p
<p>This indicates the extent that each metric can be used to predict performance.</p
Comparison results of different network models: A is training accuracy of model, B is validation acc...
F-beta was calculated as a measure of classification accuracy. See S1 Table in S1 File for optimal h...
Performance metrics of the method as a function of the detection range (m) for the PLN-ARIS dataset:...
a<p>The number of clusters in the network is determined automatically by the algorithms.</p>b<p>The ...
The scores for the precision, recall, F1-score, and accuracy metrics are shown for all four classifi...
<p>Performance comparisons of multiple individual classifiers on the training dataset by 10-fold cro...