Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers are classification probabilities, normalized to a row sum of one. The highest values are mostly on the diagonal (agreement of ground truth and prediction) or in an element next to the diagonal (a deviation with a neighboring class).</p
(1) only each prediction model after data preprocessing, where (a) LR, (c) RF, (e) GB, (g) DNN, (i) ...
Confusion matrix for the best classification model, which corresponded to a neural network with four...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
<p>The rows hereby indicate the predicted, i.e. real class, whereas the columns indicate the actual ...
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>Fraction of the test data that is assigned to each class based on the posterior probability assig...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
<p>(UA, user’s accuracy; PA, producer’s accuracy; OA, overall accuracy; Kappa, Kappa statistics; UNC...
A. Confusion matrix of predicted labels of the validation data (columns) compared to the ground trut...
The diagonal values represent the ratio of correct classifications for each word, and the off-diagon...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
(1) only each prediction model after data preprocessing, where (a) LR, (c) RF, (e) GB, (g) DNN, (i) ...
Confusion matrix for the best classification model, which corresponded to a neural network with four...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
<p>The rows hereby indicate the predicted, i.e. real class, whereas the columns indicate the actual ...
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>Fraction of the test data that is assigned to each class based on the posterior probability assig...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
<p>(UA, user’s accuracy; PA, producer’s accuracy; OA, overall accuracy; Kappa, Kappa statistics; UNC...
A. Confusion matrix of predicted labels of the validation data (columns) compared to the ground trut...
The diagonal values represent the ratio of correct classifications for each word, and the off-diagon...
The diagonal values indicate the ratio of correct classifications for each digit, while off-diagonal...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
(1) only each prediction model after data preprocessing, where (a) LR, (c) RF, (e) GB, (g) DNN, (i) ...
Confusion matrix for the best classification model, which corresponded to a neural network with four...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...