## 4 Experimental Results and Analysis
### The Degraded MFELCM  
In practice, space-based AIS data may not provide the features required by the four base classifiers at the same time. The classification effect of degraded MFELCM with one base classifier absence is discussed below.  
When the static features are missing, the degraded MFELCM (denoted as MFELCM1) integrates 1D-CNN, Bi-GRU, and XGBoost. The parameters of Random Forest2 in MFELCM1 are shown in Table 14. The weights of Random Forest2 to each base classifier are shown in Figure 32. Figure 33 shows the confusion matrixes of MFELCM1 on the testing set. Table 15 compares the performance of MFELCM1 with the base classifiers, and MFELCM1 is better than the base classifiers in terms of the total accuracy and F1 score. In addition, MFELCM1 can either refresh the classification results by updating the inputs or can switch to MFELCM when receiving static features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline max\_depth & 57 \\ max\_leaf\_nodes & 40 \\ min\_samples\_leaf & 9 \\ n\_estimators & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Parameters of Random Forest2.  
Figure 31: (**a**) Confusion matrix of samples (MFELCM); (**b**) Confusion matrix of ships (MFELCM).  
Figure 30: MFELCM feature importance.  
In the case of missing dynamic feature distribution due to insufficient dynamic data, the degraded MFELCM (noted as MFELCM2) integrates Random Forest1, Bi-GRU, and XGBoost. The parameters of Random Forest2 in MFELCM2 are shown in Table 16. The weights of Random Forest2 to each base classifier are shown in Figure 34. Figure 35 shows the confusion matrixes of MFELCM2 on the testing set. Table 17 compares the performance of MFELCM2 with the base classifiers. MFELCM2 outperforms the base classifiers in terms of the total accuracy and \(F1\) score. In addition, MFELCM2 can either refresh the classification prediction by updating the inputs or can switch to MFELCM after receiving a sufficient amount of dynamic data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Items**} & \multicolumn{4}{c}{**Evaluated on Samples**} & \multicolumn{4}{c}{**Evaluated on Ships**} \\ \cline{3-10}  & & **1D-CNN** & **Bi-GRU** & **XGBoost** & **MFELCM1** & **1D-CNN** & **Bi-GRU** & **XGBoost** & **MFELCM1** \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & class 0 & 0.9217 & 0.7191 & 0.8548 & _0.9147_ & 0.6667 & 0.6667 & 0.7778 & _0.6111_ \\  & class 1 & 0.7861 & 0.6052 & 0.5467 & _0.8316_ & 0.6271 & 0.6864 & 0.6102 & _0.6186_ \\  & class 2 & 0.8879 & 0.7690 & 0.8117 & _0.8385_ & 0.7903 & 0.7419 & 0.8065 & _0.7258_ \\  & class3 & 0.6173 & 0.5972 & 0.6839 & _0.7526_ & 0.7315 & 0.6296 & 0.7546 & _0.8241_ \\  & Total Accuracy & 0.7914 & 0.6566 & 0.7008 & _0.8331_ & 0.7077 & 0.6643 & 0.7222 & _0.7415_ \\  & F1 score & 0.7873 & 0.6629 & 0.6991 & _0.8334_ & 0.7127 & 0.6681 & 0.7239 & _0.7396_ \\ \hline \hline \end{tabular}
\end{table}
Table 15: Performance evaluation of base classifiers and MFELCM1.  
Figure 33: (**a**) Confusion matrix of samples (MFELCM1); (**b**) Confusion matrix of ships (MFELCM1).  
Figure 32: MFELCM1 feature importance.