# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
## 4 Simulation Results and Discussion
### Performance of Different Methods
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Datasets (\%)**} \\ \cline{2-10}
**Techniques** & **P** & **P** & **P** & **P** & **P** & **P** \\ \hline DenseNet-AO & 96.34 & 92.34 & 93.47 & 95.56 & 96.66 & 97.44 & 96.34 & 92.54 \\ ML-RBF & 88.34 & 88.34 & 91.47 & 89.66 & 90.01 & 89.46 & 88.34 & 91.28 \\ RAKEL & 75.21 & 82.77 & 80.24 & 78.79 & 80.56 & 78.79 & 82.6 & 83.78 \\ ECC & 67.21 & 74.77 & 72.24 & 70.79 & 72.56 & 70.79 & 74.6 & 75.78 \\ CNN & 70.21 & 77.77 & 75.24 & 73.79 & 75.56 & 73.79 & 77.6 & 78.78 \\ NB & 39.07 & 69.34 & 50.51 & 62.79 & 63.34 & 76.34 & 82.61 & 70.34 \\ BERT & 84.56 & 82.44 & 87.56 & 85.23 & 88.12 & 86.21 & 83.78 & 89.12 \\ LSTM & 79.12 & 77.89 & 80.67 & 81.34 & 82.77 & 80.24 & 79.66 & 79.66 \\ Transformer & 87.34 & 85.12 & 88.34 & 86.21 & 89.56 & 87.12 & 86.67 & 88.34 \\ GAT & 83.56 & 81.23 & 85.12 & 82.56 & 86.23 & 84.34 & 82.34 & 85.12 \\ ResNet & 91.34 & 89.12 & 92.34 & 90.45 & 91.56 & 90.67 & 89.23 & 92.34 \\ SVM & 88.12 & 85.56 & 89.56 & 87.12 & 88.34 & 87.56 & 86.78 & 89.56 \\ Logistic Regression & 86.78 & 83.21 & 87.12 & 84.56 & 86.21 & 85.23 & 84.78 & 87.12 \\ Random Forest & 89.23 & 87.34 & 90.01 & 88.67 & 89.78 & 88.12 & 87.89 & 90.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recall comparison of benchmark scheme vs. SEn. The proposed ensembler achieves a higher overall score on the movie dataset, as seen in Figure 12. Although it is a multi-labeling dataset, even the most complex method performs well. The proposed ensembler outperforms other techniques in terms of precision, f-measurement, accuracy, and recall, as depicted in Figures 13 and 14. The great accuracy implies that the proposed ensembler would produce unexpectedly strong results with multi-labeling data, which is a promising sign. ML-RBF decreases as the running output increases in Figures 11-14. From Figures 11-14, we can conclude that EDAO is improving. The divergent trends of both aims indicate that they are inherently at odds. It should not come as a surprise. Predicted baseline learner labels intersect with the labels correctly due to the ML-RBF optimization. As a result, all of these fundamental learners appear to be the same. Figure 11: Classifier accuracy on the news dataset. Figure 10: Frequency-based features of the news dataset. In contrast, ECC maximization enables base learners to exhibit maximum flexibility in terms of training error. In most situations, the stated goals are opposed to one another. The conflict allows ML-RBF to use population optimization to balance the two goals. It is crucial to note that in this situation, NB and ECC assess the basic learners' diversity and correctness, not the ensemble's overall performance. The decrease in ML-RBF does not suggest that there has been a decline in the ensemble. Despite this, the increase in EDAO implies that base learners are becoming more varied, improving group performance. An optimization technique is used to fine-tune the tuning parameters of the ensembler. The precision of multi-labeling is improved as a result of these discoveries. As a result, EDAO consistently enhances a multi-label ensemble's ability to generalize, enhancing multi-label categorization competence. The sensitivity analysis (SA) is a critical aspect of our study as it introduces the element of uncertainty into our evaluation of both benchmark schemes and our proposed algorithm. Figure 14: Accuracy of classifiers on medical dataset. Figure 12: Classifier accuracy on the movie dataset. Figure 13: Classifier accuracy on the emotion dataset. SA involves calculating the extent of change required for each uncertain element before altering the initial decision. This analysis provides valuable insights into the robustness and stability of our proposed approach. In Figure 15, we present the results of the sensitivity analysis specific to our recommended technique, DenseNet-AO. This figure showcases the best estimates for each variable within our approach, highlighting how they deviate from the baseline estimate.