# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Algorithm Performance Evaluation
The proposed RPC-EAU algorithm fully utilizes the collaborative decision-making strategy based on classifier and data distribution, which quickly outputs mutually assured results and comprehensively optimizes uncertain data. This not only improves classification accuracy but also reduces the number of iterations to achieve low computational time. ### Parameter Analysis

In the RPC-EAU, the number of nearest neighbor decision evidences \(K\) and the confidence threshold \(T_{cd}\) are two crucial parameters that have a significant impact on perfor \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multirow{2}{*}{**Sample Type**} & \multicolumn{3}{c}{**Number of Samples in Each Class**} \\ \cline{3-5}  & & \(\mathbf{\omega_{T}}\) & \(\mathbf{\omega_{c}}\) & \(\mathbf{\Theta}\) \\ \hline \multirow{2}{*}{Exp. 1} & Training samples & 191 & 122 & 48 \\  & Test samples & 181 & 141 & 38 \\ \hline \multirow{2}{*}{Exp. 2} & Training samples & 182 & 137 & 42 \\  & Test samples & 190 & 126 & 44 \\ \hline \multirow{2}{*}{Exp. 2} & Training samples & 174 & 141 & 46 \\  & Test samples & 198 & 122 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The number of training and test samples in each experiment. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Indicators**} & \multicolumn{6}{c}{**The Statistical Results of Each Algorithm**} \\ \cline{2-7}  & **PSO-SVM** & **IKNN** & **RPC-FNN** & **RPC-RNN** & **RPREC** & **RPC-EAU** \\ \hline \(CA\) & 0.860 & 0.867 & 0.918 & 0.936 & 0.958 & 0.961 \\ \(TLR\) & 0.117 & 0.099 & 0.052 & 0.043 & 0.025 & 0.024 \\ \(CRR\) & 0.835 & 0.829 & 0.887 & 0.913 & 0.942 & 0.945 \\ \(CT\)(s) & 0.21 & 0.52 & 1.13 & 2.37 & 24.21 & 2.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The average results of these three experiments. Table 8 showed the values of \(CA\), \(CRR\) and CPU time gradually decrease with the increase in \(T_{cd}\), while \(TLR\) increases in contrast. When the value of \(T_{cd}\) is 0.1, the RPC-EAU has the best classification performance in this experiment. At this time, the classification accuracy can reach 0.953, the target loss rate can reach 0.013, and the clutter removal rate can reach 0.943. But the corresponding cost is the maximum CPU time, which is 2.98 s. When the value of \(T_{cd}\) increases to 0.9, the number of correct identifications for both targets and clutter will decrease, resulting in the values of \(CA\) and \(CRR\) only being around 0.82. Of course, the advantage is that it can provide classification results faster, such as a CPU time of only 0.31. The smaller the confidence threshold, the more evidence needs to be corrected in each iteration, resulting in fewer directly output samples. This will inevitably increase the number of iterations for algorithm convergence and CPU time. But the smaller the confidence threshold, the more we need to use sample feature distribution information to correct evidences and optimize the classifier, so as to obtain better recognition results. The experimental results of various evaluation indicators in Table 3 also precisely verify this point. As shown in Figure 7, the classification accuracy \(CA\) and clutter removal rate \(CRR\) of RPC-EAU gradually increase with the increase in \(K\). Especially when k increases from 1 to 8, it can increase from around 0.2 to 0.9. This indicates that limited decision information can lead to misclassification when the number of \(K\)-nearest neighbor samples is small. So when \(K\) is less than 2, the classification accuracy and clutter removal rate are even less than 0.5. The classification accuracy and filtering rate increase rapidly when \(K\) is less than 8, and then no significant changes occur when \(K\) is greater than 8, remaining stable at around 0.94. The target loss rate \(TLR\) decreases continuously with the increase in \(K\). This indicates that the more \(K\)-nearest neighbor evidence used to assist decision-making, the less likely it is to miss target recognition information.