## 5 Conclusions and Discussion  
We have proposed the stacking method for ensemble learning to improve the classification accuracy of the classifier to recognize micro-Doppler signals. The stacking method is to combines the classification models of different structures. Also, we additionally designed and tested the modified stacking method that reflects the variability of micro-Doppler effects over time. Table 6 shows the experimental results. The stacking method of ensemble learning showed a performance improvement of about 2.6% (test accuracy) over the DNN based multiclass classifier. Figure 10b showed that the DNN based multiclass classifier could no longer improve its performance due to over-fitting and fluctuation. However, Figure 11a showed that the stacking method could be an effective alternative to optimize. In other words, the stacking method can give better learning process when the learning rate of the DNN is too long or too difficult due to its complex structure. We can get a more efficient learning process through the stacking that properly divides and combines the given structure. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Structure** & **Training Accuracy** & **Validation Accuracy** & **Test Accuracy** \\ \hline SVM (Binary) & 99.9\% & 75.8\% & 75.2\% \\ DNN (Binary) & 93.4\% & 89.7\% & 89.7\% \\ SVM (Multiclass) & 99.9\% & 71.8\% & 71.1\% \\ DNN (Multiclass) & 96.9\% & 88.7\% & 88.8\% \\ Stacking method (Multiclass) & 92.1\% & 91.5\% & 91.4\% \\ Modified stacking method (Multiclass) & 95.6\% & 95.6\% & 95.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The accuracy performances of the tested classifiers. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **(a *)** & **(b *)** & **(c *)** & **(d *)** & **(e *)** & **(f *)** & **(g *)** & **(h *)** \\ \hline (a) & 95.96 & 0.08 & 0.85 & 0.36 & 0.44 & 0.36 & 1.53 & 0.40 \\ (b) & 0.28 & 94.90 & 0.52 & 0.24 & 0.28 & 3.45 & 0.24 & 0.08 \\ (c) & 0.93 & 0.43 & 91.31 & 2.06 & 0.27 & 0.89 & 2.33 & 1.79 \\ (d) & 0.28 & 0.16 & 2.36 & 85.49 & 1.34 & 1.22 & 1.02 & 8.14 \\ (e) & 0.40 & 0.65 & 0.57 & 1.45 & 93.58 & 0.36 & 1.94 & 1.05 \\ (f) & 0.32 & 3.69 & 0.40 & 0.76 & 0.44 & 91.65 & 1.16 & 1.57 \\ (g) & 1.62 & 0.21 & 2.62 & 1.08 & 1.62 & 0.66 & 90.57 & 1.62 \\ (h) & 0.28 & 0.00 & 1.22 & 7.18 & 0.75 & 0.83 & 1.54 & 88.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The confusion matrix of the stacking method (*: Estimated class, Unit: %). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **(a *)** & **(b *)** & **(c *)** & **(d *)** & **(e *)** & **(f *)** & **(g *)** & **(h *)** \\ \hline (a) & 97.16 & 0.00 & 0.28 & 0.12 & 0.20 & 0.24 & 1.44 & 0.56 \\ (b) & 0.08 & 97.75 & 0.04 & 0.04 & 0.00 & 1.97 & 0.12 & 0.00 \\ (c) & 0.36 & 0.04 & 97.13 & 0.57 & 0.24 & 0.08 & 0.81 & 0.77 \\ (d) & 0.12 & 0.04 & 1.50 & 90.16 & 0.63 & 0.36 & 0.51 & 6.68 \\ (e) & 0.46 & 0.21 & 0.21 & 0.62 & 97.52 & 0.12 & 0.58 & 0.29 \\ (f) & 0.40 & 1.50 & 0.24 & 0.28 & 0.24 & 96.56 & 0.40 & 0.40 \\ (g) & 0.88 & 0.00 & 1.57 & 0.48 & 0.84 & 0.52 & 94.90 & 0.80 \\ (h) & 0.32 & 0.00 & 0.56 & 4.09 & 0.36 & 0.28 & 0.52 & 93.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The confusion matrix of the modified stacking method (*: Estimated class, Unit: %). Lastly, we designed and tested the modified stacking method to improve the accuracy of the classifier. The result showed a performance increase of about 6.8% (test accuracy) over the DNN based multiclass classifier. This result means that the performance can get improvement by using information that reflects the variability of micro-Doppler effects over time. If we wanted to put more FFT data into the input of the DNN based multiclass classifier, the input node would have increased by k multiple of 512. This approach may lead to an optimization problem due to a more complex structure.