<p>Confusion matrix for the classifiers of RF, SVM, and WKNN using the input dataset with all the predictor variables (UA, user’s accuracy; PA, producer’s accuracy; OA, overall accuracy; Kappa, Kappa statistics).</p
<p>Confusion matrix and overall performance of the classifier used to determine the sharpness of the...
<p>Confusion matrix for the classification of the principal Tsik class into sub-classes using OPF wi...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
<p>(UA, user’s accuracy; PA, producer’s accuracy; OA, overall accuracy; Kappa, Kappa statistics; UNC...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
<p>These are NB (A) and SVM-R (B). The color code indicates average accuracy per composition (the hi...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>Confusion matrix for classification by the statistical models versus the empirical models.</p
<p>(A), Confusion matrix of the performance of the SVM with NASs on the KTH dataset. The average acc...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
Since each classifier distinguishes between the desired class and every “other” class, the confusion...
(1) only each prediction model after data preprocessing, where (a) LR, (c) RF, (e) GB, (g) DNN, (i) ...
<p>A-P: Active-Pleasant; A-U: Active-Unpleasant; P-P: Passive-Pleasant; P-U: Passive-Unpleasant.</p>...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>Confusion matrix and overall performance of the classifier used to determine the sharpness of the...
<p>Confusion matrix for the classification of the principal Tsik class into sub-classes using OPF wi...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...
<p>(UA, user’s accuracy; PA, producer’s accuracy; OA, overall accuracy; Kappa, Kappa statistics; UNC...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
(a) RF, (b) GBM, (c) AdaBoost, (d) LR, (e) SVC, (f) SVEC-H, (g) SVEC-S, (h) CNN, (i) LSTM, (j) CNN-L...
<p>These are NB (A) and SVM-R (B). The color code indicates average accuracy per composition (the hi...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>Confusion matrix for classification by the statistical models versus the empirical models.</p
<p>(A), Confusion matrix of the performance of the SVM with NASs on the KTH dataset. The average acc...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
Since each classifier distinguishes between the desired class and every “other” class, the confusion...
(1) only each prediction model after data preprocessing, where (a) LR, (c) RF, (e) GB, (g) DNN, (i) ...
<p>A-P: Active-Pleasant; A-U: Active-Unpleasant; P-P: Passive-Pleasant; P-U: Passive-Unpleasant.</p>...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>Confusion matrix and overall performance of the classifier used to determine the sharpness of the...
<p>Confusion matrix for the classification of the principal Tsik class into sub-classes using OPF wi...
<p>Confusion matrix of the classification results using a NaiveBayes classifier and cross validation...