## 4 Experimental Results and Analysis
### Ship Classification Considering Geometric Features and Behavior Characteristics  
According to the discussion in the previous section, the classification accuracy of the five types of ships can reach approximately 70% when the geometric features are considered. In this section, the geometric features \(f_{\delta}\) and behavior characteristics \(f_{m}\) are considered simultaneously--that is, the input of the SVM and the random forest is a 13-dimensional feature vector. The classification accuracy of the classifiers on the test dataset is presented in Table 6, and the maximum classification accuracy is marked in bold. The classification accuracy of the SVM and random forest reached 87.40% and 92.70%, which are significantly higher than those of the models when only the geometric features are considered. In addition, the other evaluation metrics of the classifiers have also been improved by adding behavior characteristics. For example, when the random forest model is used during the experiment, the \(F_{1}\)-_Score_ of the five types of ships reaches 92.80%, 92.11%, 92.91%, 93.88%, and 93.20%, respectively. It can also be found that the overall classification result of the RF model is better than that of the SVM, which shows strong performance in the AIS data ship classification tasks. The experiments show that the behavior characteristics constructed in this paper are effective in the task of AIS data ship classification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Ship Types**} & \multicolumn{6}{c}{**Evaluation Metrics**} \\ \cline{3-8}  & & _Precision_ & _Recall_ & \(F_{1}\)**-_Score_ & **Support Size_ & _Accuracy_ \\ \hline \multirow{4}{*}{SVM} & Cargo & 83.72\% & 90.00\% & 86.75\% & 200 & \\  & Tanker & 89.80\% & 88.00\% & 88.89\% & 200 & \\  & Fishing & 83.11\% & 91.00\% & 86.88\% & 200 & \\  & Passenger & 91.71\% & 83.00\% & 87.14\% & 200 & \\  & Tug & 89.95\% & 85.00\% & 87.40\% & 200 & \\ \hline \multirow{4}{*}{RF} & Cargo & 92.12\% & 93.50\% & 92.80\% & 200 & \\  & Tanker & 93.78\% & 90.50\% & 92.11\% & 200 & \\ \cline{1-1}  & Fishing & 90.91\% & 95.00\% & 92.91\% & 200 & **92.70\%** \\ \cline{1-1}  & Passenger & 95.83\% & 92.00\% & 93.88\% & 200 & \\ \cline{1-1}  & Tug & 93.91\% & 92.50\% & 93.20\% & 200 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The classification results of the five types of ships by considering both the geometric features and behavior characteristics.  
Figure 4: The confusion matrix of the five types of ship test samples considering the geometric features. (**a**) The confusion matrix of the SVM; (**b**) the confusion matrix of the RF.  
Figure 5 shows the classification confusion matrixes of the five types of ships for the SVM and RF models. Due to the similarity in the spatial distribution of features between cargo ships and tanker ships, as well as fishing ships, passenger ships, and tug ships, the output of the classifiers still has classification confusion. However, comparing Figures 4 and 5, it can be seen that the misclassification of the five types of ships, especially passenger ships and tugs, has been significantly improved by adding behavior characteristics. From the experimental results presented in Sections 4.1 and 4.2, it can be seen that the performance of the ship classification models is significantly improved when the extracted behavior characteristics are used, which demonstrates the effectiveness of our method.