## 5 Experimental Results
### Cascade Approach
#### 5.4.4 Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**k-NN**} & \multicolumn{2}{c}{**ANN**} & \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]** & **Accuracy [\%]** & **Time [ms]** \\ \hline
**Root Dataset** & 67.91 (3.29) & 32.94 (1.74) & **69.39 (2.78)** & 1.01 (0.17) & 48.92 (6.49) & 0.72 (0.133) & 45.68 (6.43) & 0.90 (0.37) & 54.46 (2.89) & 1.42 (0.85) \\
**Airport** & **83.75 (4.11)** & 2.78 (0.65) & 79.06 (5.11) & 0.22 (0.05) & 70.94 (7.07) & 0.70 (1.05) & 74.69 (6.66) & 0.80 (0.18) & 69.38 (7.91) & 0.65 (0.16) \\
**Construction** & 75.00 (9.53) & 1.75 (0.85) & 81.11 (6.52) & 0.19 (0.10) & 62.22 (0.36) & 0.53 (0.14) & 68.89 (9.14) & 0.70 (0.13) & **89.44 (9.61)** & 0.59 (0.11) \\
**Residence** & 79.50 (0.31) & 1.061 (0.33) & **82.75 (43.20)** & 0.44 (0.00) & 66.63 (38.48) & 0.91 (0.48) & 71.00 (0.38) & 70.76 (0.26) & 76.25 (0.79) & 0.87 (0.09) \\
**Vehicles** & **83.51 (4.84)** & 3.27 (0.41) & 80.00 (8.56) & 0.41 (0.25) & 68.92 (9.38) & 0.57 (0.10) & 73.24 (4.11) & 0.83 (0.34) & 77.84 (0.04) & 0.60 (0.07) \\
**Warning** & 74.64 (6.62) & 2.11 (0.24) & **76.07 (4.47)** & 0.31 (0.06) & 65.00 (8.72) & 0.64 (0.15) & 65.36 (5.84) & 0.99 (0.74) & 65.36 (10.79) & 0.58 (0.03) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Achievable accuracy and demanded execution time for the proposed cascade approach of the classifiers. The most accurate classifier per dataset is marked in bold. The Standard Deviation (SD) is in brackets. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**k-NN**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**Most Accurate**} & \multicolumn{2}{c}{**Fastest**} \\ \cline{3-10}
**Feature Set** & **Path** & \multicolumn{2}{c}{**Overall**} & \multicolumn{2}{c}{**Overall**} & \multicolumn{2}{c}{**Time [ms]**} & \multicolumn{2}{c}{**Accuracy [\%]**} & \multicolumn{2}{c}{**Time [ms]**} & \multicolumn{2}{c}{**Accuracy [\%]**} & \multicolumn{2}{c}{**Time [ms]**} \\ \cline{3-10}  & **Accuracy [\%]** & **Time [ms]** & **Accuracy [\%]** & **Time [ms]** & **Accuracy [\%]** & **Time [ms]** & **Accuracy [\%]** & **Time [ms]** \\ \hline \multirow{5}{*}{Set 3} & **Root Dataset \(\Rightarrow\) Airport** & 56.87 & 35.73 & 54.86 & 1.22 & 58.12 & 3.79 & 38.68 & 0.93 \\  & **Root Dataset \(\Rightarrow\) Construction** & 50.93 & 34.69 & 56.28 & 1.20 & 62.07 & 1.60 & 39.68 & 0.91 \\
**Root Dataset \(\Rightarrow\) Residence** & 53.98 & 43.56 & 57.42 & 1.44 & 57.42 & 1.44 & 40.48 & 1.15 \\
**Root Dataset \(\Rightarrow\) Vehicles** & 56.71 & 36.21 & 55.51 & 1.41 & 57.95 & 4.28 & 39.14 & 1.12 \\
**Root Dataset \(\Rightarrow\) Warning** & 50.69 & 35.05 & 52.79 & 1.32 & 52.79 & 1.32 & 37.21 & 1.03 \\ \hline
**Mean** & **53.84** & **37.05** & **55.37** & **1.32** & **57.67** & **2.48** & **39.04** & **1.03** \\ \hline
**SD** & 2.99 & 3.69 & 1.73 & 0.11 & 3.30 & 1.43 & 1.22 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Overall accuracy and timing in milliseconds of the cascade approach for the optimized k-NN and ANN classifiers. The average values are marked in bold.