# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 4 Experiments and Results
### Experimental Results
#### 4.2.1 The Effect of Base Learners  
First, we performed experiments with individual CNN models; the results are shown in Table 3. The ResNet152, DenseNet201, and EfficientNetB0 models performed better than the ResNet50_CA, ResNet50_SE, and ResNet50_NL models.  
Next, we performed experiments to show the effectiveness of the proposed ensemble classifier (ensemble classifier 2) in comparison with ensemble classifier 1 based on the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Zoom Range** & **Rotation Range** & **Shear Range** & **Horizontal Flip** \\ \hline
0.3 & 30 & 0.2 & True \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric transformation.  
ResNet152, DenseNet201, and EfficientNetB0 models. Figure 8 and Table 3 show the performance of the individual models along with the performance of ensemble classifiers. As shown in the figure, the accuracy of individual models does not exceed 94%, while the ensemble classifiers show improvement in performance. Ensemble classifier 1, based on the three popular high-performance models (ResNet152, DenseNet101, and EfficientNetB0), enhances the accuracy by about 0.6%, as shown in Figure 8a.  
However, ensemble classifier 2, which integrates the pre-trained ResNet50 with the SE attention block, ResNet50 with the CA block, and ResNet50 with the NL block, improves the accuracy up to 4%, as shown in Figure 8b. Though the performance of the base learners in the case of ensemble classifier 2 is not better than that of the ResNet152, DenseNet201, and EfficientNetB0 models, the performance of ensemble classifier 2 is better than that of ensemble classifier 1. This is in accordance with the theory of ensemble learning, which recommends the use of weak and diverse base learners. As the ResNet50_CA, ResNet50_SE, ResNet50_NL models exhibit weak and diverse performances, as shown in Table 3, ensemble classifier 2, based on these models, significantly outperforms ensemble classifier 1. The Venn diagrams in Figure 9 show the error analysis of each individual base learner. The non-overlapping areas represent samples misclassified by only one learner,  
Figure 8: The performance of two ensemble classifiers. (**a**) The performance in terms of accuracy of ensemble learner 1 and the base learners. (**b**) The performance in terms of accuracy of ensemble learner 2 and the base learners.  
while the two-circle overlap indicates samples misclassified by two learners, with the possibility that the third learner correctly classifies them. A three-circle overlap signifies that all three learners failed to classify those samples. Figure 9b shows a slight overlap between the errors made by the base learners of ensemble classifier 2. While ResNet50 with the SE block has mostly unique errors, ResNet50 learners with the NL and CA blocks share about 50% of their errors. Analyzing the contributions of each classifier using the proposed entropy-based voting method, the base learners with the SE, NL, and CA blocks contributed to 23%, 47%, and 30% of the classifications, with error rates of 0.8%, 2.3%, and 1.8%, respectively. In contrast, Figure 9a shows a greater overlap between the errors of the base learners, indicating fewer opportunities for the ensemble classifier to improve performance on these samples. Furthermore, using the proposed entropy-based voting method, the base learners contribute with proportions of about 89%, 9%, and 2% for ResNet152, DenseNet, and EfficientNet, respectively. The dominance of ResNet152, combined with the increased overlap, explains the limited performance improvement of ensemble classifier 1.