## 5 Discussion and Future Work  
Comparison of Performance Metrics between Predictive models Using Significant Features and Full Features  
To verify that full features are replaced by the significant features selected by the feature selection method, and for the dynamic fire risk of the stadium to be effectively characterized, in this experiment, a set of six prediction algorithms, MLP, SVM, RF, Bagging, Adaboost and GBDT, for the prediction of fire risk level were used. Using two cross-validation strategies (choose CV = 5), we can calculate the average performance metrics for \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Performance Metrics**} & \multirow{2}{*}{**Machine Learning**} & \multicolumn{2}{c}{**K-Fold Cross-Validation**} & \multicolumn{2}{c}{**Stratified K-Fold Cross-Validation**} \\ \cline{3-6}  & & **Full Features** & **RFE-Top20** & **Full Features** & **RFE-Top20** \\  & & **(47 Features)** & **(17 Features)** & **(47 Features)** & **(17 Features)** \\ \hline \multirow{6}{*}{(a) Accuracy} & MLP & 87.1 & 83.5 & 86.0 & 87.4 \\  & SVM & 86.7 & 86.7 & 90.1 & 90.1 \\  & RF & 91.6 & 92.1 & 91.3 & 91.9 \\  & Bagging & 92.4 & 92.1 & 91.8 & 90.4 \\  & AdaBoost & 91.8 & 91.8 & 90.2 & 89.6 \\  & Gradient Boosting & 93.2 & 93.2 & 91.6 & 90.7 \\ \hline \multirow{6}{*}{(b) Precision} & MLP & 68.5 & 54.4 & 68.8 & 71.9 \\  & SVM & 71.0 & 71.0 & 75.4 & 75.4 \\ \cline{1-1}  & RF & 77.5 & 81.5 & 80.2 & 81.5 \\ \cline{1-1}  & Bagging & 82.5 & 79.8 & 81.0 & 79.0 \\ \cline{1-1}  & AdaBoost & 81.9 & 81.9 & 77.6 & 74.7 \\ \cline{1-1}  & Gradient Boosting & 84.2 & 84.2 & 80.7 & 78.8 \\ \hline \multirow{6}{*}{(c) Recall} & MLP & 58.8 & 48.0 & 63.1 & 67.7 \\  & SVM & 70.8 & 70.8 & 73.4 & 73.4 \\ \cline{1-1}  & RF & 80.0 & 80.8 & 79.5 & 80.9 \\ \cline{1-1}  & Bagging & 82.9 & 81.4 & 80.0 & 77.5 \\ \cline{1-1}  & AdaBoost & 80.5 & 80.5 & 77.3 & 73.3 \\ \cline{1-1}  & Gradient Boosting & 84.3 & 84.3 & 80.4 & 78.6 \\ \hline \multirow{6}{*}{(d) F1- score} & MLP & 61.3 & 48.0 & 64.1 & 68.4 \\ \cline{1-1}  & SVM & 65.2 & 67.7 & 72.1 & 72.1 \\ \cline{1-1}  & RF & 76.4 & 77.8 & 77.8 & 79.6 \\ \cline{1-1}  & Bagging & 80.1 & 78.0 & 79.0 & 76.5 \\ \cline{1-1}  & AdaBoost & 78.6 & 78.4 & 77.3 & 71.9 \\ \cline{1-1}  & Gradient Boosting & 81.9 & 81.9 & 78.7 & 77.0 \\ \hline \multirow{6}{*}{(e) Auroc} & MLP & 91.4 & 88.8 & 90.1 & 92.1 \\ \cline{1-1}  & SVM & 94.8 & 94.8 & 95.4 & 95.3 \\ \cline{1-1}  & RF & 95.9 & 95.8 & 96.1 & 95.9 \\ \cline{1-1}  & Bagging & 95.7 & 94.9 & 94.1 & 94.5 \\ \cline{1-1}  & AdaBoost & 93.4 & 93.5 & 91.9 & 91.7 \\ \cline{1-1}  & Gradient Boosting & 96.2 & 95.9 & 95.1 & 95.1 \\ \hline \multirow{6}{*}{(f) Auroc} & MLP & 76.6 & 68.9 & 75.1 & 77.7 \\ \cline{1-1}  & SVM & 84.8 & 84.9 & 88.2 & 87.9 \\ \cline{1-1}  & RF & 86.2 & 86.2 & 87.7 & 86.0 \\ \cline{1-1}  & Bagging & 86.8 & 87.1 & 84.6 & 85.7 \\ \cline{1-1}  & AdaBoost & 84.0 & 84.2 & 78.2 & 78.2 \\ \cline{1-1}  & Gradient Boosting & 89.8 & 88.8 & 84.4 & 84.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy, precision, recall, F1-score, auroc, and auroc using the full features and selected features on the stadium fire dataset using five-fold cross-validation. each of the classifiers, namely recall, F1-score, aurroc and auprc. Figure 10a,b shows that, in the case of stratified k-fold cross-validation, based on recall, F1-score, aurroc and auprc, all models using significant features are almost better than full features. In the case of k-fold cross-validation, the performance of all models using significant features is slightly lower than that of full features, such as in the MLP model, the recall of significant features is 48.0%, while the recall of full features is 58.8%. The F1 score of significant features is 48.0%, while the F1 score of full features is 61.3%. In addition, in the Gradient Boosting model, the auprc of the full feature is 89.8%, which is slightly higher than the auprc of the significant feature of 88.8%.