## 4 Experimental Results and Analysis
### Base Classifiers and MFELCM
#### 4.2.2 1D-CNN Experimental Results  
The number and the width of the convolution kernels in the first convolution layer (Conv_1) in 1D-CNN are optimized using grid search, as shown in Table 9. The model used an Adam optimizer with a learning rate of 1e-3, a batch size of 2500, and the cross-entropy loss function. Figure 10 shows the structure of 1D-CNN. 1D-CNN performs best when the parameter of Conv_1 is Conv (30,15). Figure 19: Static feature importance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters of Conv1**} & \multicolumn{2}{c}{**F1 Score**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-5}  & **Evaluated on Samples** & **Evaluated on Ships** & **Evaluated on Samples** & **Evaluated on Ships** \\ \hline Conv (10,5) & 0.5758 & 0.5373 & 0.6068 & 0.5169 \\ Conv (10,10) & 0.6248 & 0.5883 & 0.6530 & 0.5749 \\ Conv (10,15) & 0.6131 & 0.5533 & 0.6538 & 0.5483 \\ Conv (10,20) & 0.6385 & 0.5590 & 0.6796 & 0.5580 \\ Conv (10,25) & 0.6380 & 0.5809 & 0.6759 & 0.5797 \\ Conv (20,5) & 0.6244 & 0.6010 & 0.6463 & 0.5797 \\ Conv (20,10) & 0.7218 & 0.6276 & 0.7467 & 0.6232 \\ Conv (20,15) & 0.7274 & 0.6378 & 0.7451 & 0.6353 \\ Conv (20,20) & 0.7167 & 0.6844 & 0.7217 & 0.6739 \\ Conv (20,25) & 0.7660 & 0.6739 & 0.7748 & 0.6715 \\ Conv (30,5) & 0.6907 & 0.6719 & 0.6973 & 0.6546 \\ Conv (30,10) & 0.6845 & 0.6342 & 0.7036 & 0.6280 \\ _Conv (30,15)_ & **0.7873** & **0.7128** & **0.7914** & **0.7077** \\ Conv (30,20) & 0.7388 & 0.7121 & 0.7398 & 0.7029 \\ Conv (30,25) & 0.7717 & 0.6754 & 0.7786 & 0.6715 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameters of the first convolution layer. Figure 18: Visualization of static features. Figure 20 plots the learning curve of 1D-CNN. Figure 21 shows the confusion matrixes of 1D-CNN on the testing set, where the classifier tends to confuse passenger ships with fishing boats, as well as tankers with cargo ships. Figure 21(a) Confusion matrix of samples (1D-CNN); (b) Confusion matrix of ships (1D-CNN). Figure 22a visualizes 30 convolutional kernels of Conv_1, each of which is a 15 \(\times\) 13 matrix. Figure 22b shows the matrix Q added to by the 30 convolution kernels in Conv_1. Figure 22c shows the result of taking the absolute value after summing the matrix Q according to the columns. Convolution realizes the dimension reduction and feature extraction from the original data. In the first convolution layer, the value of the convolution kernel in different columns reflects the response intensity of the network to different characteristics. By summing Q and taking the absolute value, the importance of features for 1D-CNN can be inferred. In Figure 22c, the features from 0 to 12 correspond to the features of \(DFD^{l=}\) in Figure 8. As we can see from Figure 22c, the four most important features to 1D-CNN are \(\delta lat\), \(\delta t\), \(\delta SOG\), and \(\delta lon\). The combination of \(\delta lat\) and \(\delta lon\) can reflect the directional information of ship motion. According to Table A1, \(\delta t\) is associated with ships motion state, and the combination of these four features (i.e., \(\delta lat\), \(\delta t\), \(\delta SOG\), and \(\delta lon\)) can reflect the information of ship's speed, acceleration, and steering rate. In Figure 4, it is obvious that there are some routes for cargo ships and tankers around the world, and the ship's direction within the routes is usually fixed. However, the movements of fishing boats and passenger ships are more variable. Based on the above analysis, we speculate that the 1D-CNN network may learn the movement characteristics of different types of ships on the routes. In addition, the time feature contributes little to 1D-CNN, which is probably because most of the ships do not have such features, except for some offshore or inland river ships, which have regular activity periods in a day.