# Ontology with Deep Learning for Forest Image Classification
## 8 Experimental Results
As illustrated in Figure 11, the RF-based model correctly classified all nineteen test images for classes 5 and 7. Only one test image for class 0 was misclassified into class 1. The RF model performed the worst for class 2, where seven test images were misclassified into other classes. The ROC AUC curves for the RF-based classifier presented in Figure 12 produced perfect matches for classes 0, 5, and 7. These results also go in tandem with the corresponding confusion matrix results in Figure 11. All the classes have ROC AUC values that are greater than 0.9, implying that the model performed better. Overall, As shown in Figure 13, the decision-tree-based model registered the worst performance as compared to the other models for all the classes, except class 5, where all nineteen test images were correctly classified despite the fact that the same class received nine more test images from other classes. The corresponding ROC AUC curves for the decision tree in Figure 14 show that class 5 with its ROC AUC of 0.96 performed the best, and class 2 performed the worst with its ROC AUC of 0.75. This is also in line with the results obtained from the corresponding confusion matrix. The confusion matrix of the SVM-based model presented in Figure 15 shows that a range of two to three test images out of nineteen was misclassified for classes 0, 1, 4, 5, 6, and 7. Class 2 had the worst performance with regard to the number of misclassified test images; five test images were misclassified into classes 1, 5, and 6. The associated ROC  
Figure 10: ROC AUC Curves for ontological-bagging-based classifier. Figure 9: Ontological-bagging-based confusion matrix. AUC curves of the SVM-based model in Figure 16 show that class 1 with its ROC AUC value of 0.95 performs the worst, as it registered fourteen false-positive test images. Figure 11: Random-forest-based confusion matrix. Figure 12: ROC AUC Curves for Random-Forest-based classifier. Figure 13: Decision-tree-based confusion matrix. The GaussianNB-based model presented in Figure 17 classified all nineteen test images for class 0 and class 5 despite class 5 receiving four extra test images, two from class 2  
Figure 16: ROC AUC Curves for Support-Vector-Machine-based classifier. Figure 8:0 ROC AUC Curves for Decision-tree-based classifier. Figure 8:1 Support-Vector-Machine-based confusion matrix. and two from class 6, and class 0 receiving 6 extra test images from classes 1, 2, 3, 4 and 7. However, the GaussianNB under-performed in classes 1 and 6, misclassifying 16 and 15 test images, respectively. Most of the test images for class 1 were misclassified into class 3, implying that most degraded land images were mistakenly viewed as logged forest images. With reference to the ROC AUC curves presented in Figure 18, the GaussianNB-based model performed best for classes 0 and 5 and performed extremely poorly for class 2. These findings also go in hand with the confusion matrix results presented in Figure 8:2  
Table 6 shows that our proposed ontological bagging approach outperformed other classifiers in terms of accuracy, RMSE, and ROC_AUC. The results also demonstrate that our model has the strongest predictive power as it managed to correctly classify 146 out of 152 test images, followed by SVM, which correctly classified 135 test images. Our model registered the lowest RMSE of 0.532, implying that the model's predictions are much closer to the actual values as compared to other models. Alongside RF, our ontological bagging algorithm recorded the highest ROC_AUC value of 0.99, meaning that the model did well in separating classes as compared to other models. GaussianNB performed the worst out of all the classifiers in terms of ROC_AUC and accuracy and misclassified 55 test images into the wrong classes. The outright performance of our model is attributed to the adoption of semantic relationships between image categories for the classification process; additionally, the bagging concept helped to minimize the error propagation of classifiers.