# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 3 Results
### Performance Comparison with other Algorithms
#### 3.3.3 Overall Performance Comparison  
In this section, we evaluated the performance of our proposed algorithm and the two comparison algorithms on four datasets as a whole. The detail experiments results are shown in Table 9.  
From Table 9 we obtain the following main conclusions:  
* In indoor and outdoor scenes, three indoor and outdoor recognition algorithms have high accuracy. Compared with the other two algorithms, the accuracy of our detection algorithm was 0.0158 higher than that of the other two algorithms in Dataset_1, and algorithm [22] was 0.0096 higher than our algorithm in Dataset_3.
* In indoor and outdoor transition scenes, our detection algorithm is superior to the other two algorithms regarding accuracy and transition delay. The detection accuracy of our algorithm on Dataset_2 and Dataset_4 is 0.1387 and 0.1308 higher than SatProbe. The indoor to outdoor transition delay of out algorithm on Dataset_2 and Dataset_4 is 5 s and 4 s faster than that of SatProbe. Furthermore, the outdoor to indoor transition delay of our algorithm on Dataset_2 and Dataset_4 is 15 s and 10 s faster than that of SatProbe.  
Figure 18: The cumulative probability of transition delay using algorithm [22] on Dataset_2 and Dataset_4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Algorithm** & **Accuracy** & \
\begin{tabular}{c} **Indoor to Outdoor transition delay when the** \\ **cumulative probability reaches 0.8** \\ \end{tabular} &
\begin{tabular}{c} **Outdoor to Indoor Transition delay when the** \\ **cumulative probability reaches 0.8** \\ \end{tabular} \\ \hline \multirow{2}{*}{**Dataset\_1**} & Proposed & 0.9911 & \multirow{2}{*}{No transition delay} & \multirow{2}{*}{No transition delay} \\  & SatProbe & 0.9619 & & \\ \cline{2-3}  & [PERSON] et al. [22] & 0.9753 & & \\ \hline \multirow{2}{*}{**Dataset\_2**} & Proposed & 0.9453 & 3s & 3s \\  & SatProbe & 0.8066 & 8s & 18s \\ \cline{2-3}  & [PERSON] et al. & 0.8778 & 7s & 12s \\ \hline \multirow{2}{*}{**Dataset\_3**} & Proposed & 0.9702 & \multirow{2}{*}{No transition delay} & \multirow{2}{*}{No transition delay} \\  & SatProbe & 0.9720 & & \\ \cline{2-3}  & [PERSON] et al. & 0.9798 & & \\ \hline \multirow{2}{*}{**Dataset\_4**} & Proposed & 0.9280 & 4s & 4s \\ \cline{2-3}  & SatProbe & 0.9792 & 8s & 14s \\ \cline{2-3}  & [PERSON] et al. & 0.8734 & 4s & 10s \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy and transition delay evaluation of different algorithms on four datasets.  
* The detection accuracy of our algorithm on Dataset_2 and Dataset_4 is 0.0675 and 0.0546 higher than algorithm [22]. The indoor to outdoor transition delay of our algorithm on Dataset_2 is 4 s faster than that of the algorithm [22]. Furthermore, the outdoor to indoor transition delay of our algorithm on Dataset_2 and Dataset_4 is 9 s and 6 s faster than that of the algorithm [22].
* From the above data, we can find that the indoor and outdoor detection algorithm we proposed can accurately identify the indoor and outdoor state in a complex environment. Especially, our algorithm enables the ability to quickly detect the indoor and outdoor transition in 4 s with a probability of more than 0.8 that other algorithms cannot.