## 5 Experimental Results
### Evaluation on an Embedded System
#### 5.5.3 Cascade Approach on an Embedded System
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Set**} & \multirow{2}{*}{**Path**} & \multicolumn{2}{c}{**k-NN**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**Most Accurate**} & \multicolumn{2}{c}{**Fastest**} \\ \cline{3-10}  & & **Overall** & \multicolumn{2}{c}{**Overall**} & \multicolumn{2}{c}{**Overall**} & \multicolumn{2}{c}{**Accuracy [\%]**} & \multicolumn{2}{c}{**Time [ms]**} & \multicolumn{2}{c}{**Accuracy [\%]**} & \multicolumn{2}{c}{**Time [ms]**} & \multicolumn{2}{c}{**Accuracy [\%]**} & \multicolumn{2}{c}{**Time [ms]**} \\ \hline \multirow{4}{*}{Set 3} & **Root Dataset \(\Rightarrow\) Airport** & \(56.50\) & 315.71 & 57.48 & 86.32 & 58.55 & 99.19 & 39.58 & 7.69 \\  & **Root Dataset \(\Rightarrow\) Construction** & 46.00 & 307.39 & 52.24 & 84.77 & 57.19 & 88.83 & 35.97 & 6.14 \\  & **Root Dataset \(\Rightarrow\) Residence** & 49.84 & 377.52 & 56.79 & 99.04 & 56.79 & 99.04 & 33.32 & 9.75 \\  & **Root Dataset \(\Rightarrow\) Vehicles** & 55.31 & 318.81 & 56.94 & 92.97 & 57.32 & 102.29 & 36.91 & 9.52 \\  & **Root Dataset \(\Rightarrow\) Warning** & 43.76 & 311.37 & 48.53 & 89.37 & 48.53 & 89.37 & 32.74 & 9.94 \\ \hline \multirow{4}{*}{Set 3} & **Mean** & **50.28** & **326.16** & **54.40** & **90.49** & **55.68** & **95.74** & **35.71** & **8.61** \\  & **SD** & 5.59 & 29.03 & 3.90 & 5.71 & 4.05 & 6.21 & 4.90 & 1.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Achievable accuracy and demanded execution time of the optimized classifiers performance using the proposed cascade approach running on the Raspberry Pi. The last row details the mean and the standard deviation (SD) of the global accuracy and timing. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{} & \multicolumn{2}{c}{**k-NN**} & \multicolumn{2}{c}{**Decision Tree**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**Naive Bayes**} \\ \cline{2-11}  & **Accuracy [\%]** & **Time [ms]** & **Accuracy [\%]** & **Time [ms]** & **Accuracy [\%]** & **Time [ms]** & **Accuracy [\%]** & **Time [ms]** \\ \hline
**Root Dataset** & 66.23 (3.16) & 300.18 (33.76) & **68.63 (3.52)** & 83.66 (33.62) & 47.26 (5.39) & 5.03 (0.32) & 51.30 (5.01) & 7.26 (0.54) & 53.22 (3.86) & 15.86 (0.98) \\
**Airport** & **85.31 (4.89)** & 55.33 (2.42) & 83.75 (5.67) & 2.66 (0.88) & 74.06 (9.20) & 3.46 (2.29) & 76.25 (5.52) & 4.93 (1.60) & 73.44 (6.95) & 58.48 (0.55) \\
**Construction** & 69.44 (6.28) & 7.21 (0.17) & 76.11 (9.39) & 1.17 (0.06) & 61.67 (0.168) & 5.85 (0.3) & 68.99 (11.15) & 5.80 (1.20) & **83.33** (7.39) & 5.17 (0.17) \\
**Residence** & 75.25 (3.63) & 77.34 (4.27) & **82.75 (3.52)** & 15.38 (1.19) & 75.0 (6.85) & 4.72 (0.31) & 68.63 (8.16) & 6.20 (1.76) & 74.13 (3.82) & 9.58 (0.49) \\
**Vehicles** & **83.51 (6.17)** & 18.63 (2.37) & 82.97 (8.06) & 9.32 (0.44) & 78.11 (8.00) & 4.49 (0.29) & 62.97 (9.01) & 4.32 (0.21) & 75.95 (8.49) & 59.97 (0.41) \\
**Warning** & 66.07 (12.05) & 11.20 (2.22) & **70.71 (11.64)** & 5.71 (0.22) & 69.29 (7.37) & 4.91 (1.57) & 62.50 (8.79) & 4.84 (1.49) & 63.39 (8.98) & 5.64 (0.39) \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Achievable accuracy and demanded execution time for the proposed cascade approach of the classifiers running on the Raspberry Pi. The most accurate classifier per dataset is marked in bold.