# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 3 Proposed Method
### Algorithm Performance Evaluation
However, the evidence adaptive updating and classifier optimization in RPC-EAU rely on the parameters such as confidence thresholds and the number of decision evidences. The correct setting of these parameters for different application scenarios is a challenge. In the future, how to reasonably set algorithm parameters to obtain good results will be a very interesting research topic. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Radar Plots**} & \multicolumn{4}{c}{**Class Label**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-5}  & \(\omega_{T}\) & \(\omega_{c}\) & \(\Theta\) & \\ \hline Number & 142 & 1531 & \(\backslash\) & 1673 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The number of radar plots for each class in this application instance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Indictors**} & \multicolumn{4}{c}{**The Statistical Results of Each Algorithm**} \\ \cline{2-7}  & **PSO-SVM** & **IKNN** & **RPC-FNN** & **RPC-RNN** & **RPEC** & **RPC-EAU** \\ \hline \(T^{Y}\) & 124 & 130 & 134 & 135 & 140 & 140 \\ \(T^{N}\) & 18 & 12 & 8 & 7 & 2 & 2 \\ \(C^{Y}\) & 1309 & 1284 & 1373 & 1382 & 1434 & 1452 \\ \(C^{N}\) & 222 & 247 & 158 & 149 & 97 & 79 \\ \(CA\) & 0.857 & 0.845 & 0.901 & 0.907 & 0.941 & 0.952 \\ \(TLR\) & 0.127 & 0.089 & 0.062 & 0.053 & 0.021 & 0.019 \\ \(CRR\) & 0.855 & 0.839 & 0.897 & 0.903 & 0.937 & 0.949 \\ \(CT\)(s) & 0.37 & 0.92 & 3.39 & 3.59 & 33.79 & 3.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The experimental results on various indicators. **Author Contributions:** Writing--original draft preparation, R.Y. ; writing--review and editing, Y.Z. ; data curation, R.Y. ; validation, R.Y. and Y.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded in part by the National Natural Science Foundation of China, grant number 61804120, and in part by the Natural Science Basic Research Program of Shaanxi, grant number 2021 JQ-515. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The processed data required to reproduce these findings cannot be shared, as the data also form part of an ongoing study. **Acknowledgments:** The authors are grateful to the reviewers for all their remarks that helped us to clarify and improve the quality of this paper. **Conflicts of Interest:** The authors declare no conflicts of interest. ## References

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