# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
## 3 Results  
This study performed more than 16 experiments while wireless clients participated in video conferencing group calls. Four datasets comprising 802.11-specific network performance parameters labeled with mean opinion score (MOS) classes in the range of 1-4 were produced to train seven ML algorithms for estimating the PQoS of video conferencing sessions in homes and buildings. Initially, each dataset comprised more than 7800 instances, and each instance consisted of more than 240 features, which increased the computational complexity of ML implementation. In light of this complexity, specific algorithms, such as multilayer perceptron (MLP), required hours of training, and it was possible to implement the model on Wi-Fi APs. Hence, after the feature selection phase using PSOSearch, 33; 62; 94, and 65 features were selected for 2n, 2 ax, 5 ac, and 5 ax datasets, respectively. Datasets representing the 802.11 ax standard experienced significant feature reduction; in the end, their features were reduced by nearly 80% compared with those of the full-feature dataset. Table 1 presents the number of instances, missing values, features, and distribution of MOS labels in each dataset. As shown in Table 1, the number of instances labeled with an MOSof 4 constituted more than 50% of the datasets, suggesting that clients experienced good quality most of the time. Notably, the 5 ax dataset contained the highest rate of missing values because wireless clients were often disconnected from the AP due to poor wireless channel conditions during the experiments. As a result, client-related 802.11 performance parameters were missing. This study utilized the ZeroR algorithm as a baseline to compare the classification accuracy of candidate algorithms with the majority class prediction approach. For example, because 67% of 802.11 ax 2.4 GHz (2 ax) instances were labeled as MOS = 4, the accuracy of the candidate algorithms should have been greater than 68%; if these conditions were not met, using complicated ML techniques would have been a waste of resources. Hence, the classification accuracies of J48, LMT, REPTree, NBTree, RIDOR and MLP algorithms were compared with ZeroR baseline. Figure 2 presents the classification accuracies of seven ML algorithms, each trained with four different datasets. The accuracy of ZeroR for predicting MOS = 4 as the baseline algorithm was more than 56%, indicating that wireless clients experienced good quality in more than half of their video conferencing sessions. Indeed, six of the algorithms could achieve substantially better classification accuracies than ZeroR, with more than 92% classification accuracy in all datasets after feature reduction. Along with classification accuracy, two other criteria--interpretability and computational cost--were included for further efficiency assessment. Classification accuracy is, after all, an overall performance metric and cannot provide a precise assessment of ML algorithms' performance in predicting different classes. Therefore, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **\#Instances** &
\begin{tabular}{c} **\#Missing** \\ **Values** \\ \end{tabular} & **\#Features** & **\#MOS = 1** & **\#MOS = 2** & **\#MOS = 3** & **\#MOS = 4** \\ \hline
2n & 10,062 & 99 & 240 & 868 & 95 & 206 & 8893 \\
2 ax & 12,369 & 1982 & 294 & 3830 & 111 & 148 & 8280 \\
5 ac & 8538 & 303 & 241 & 3555 & 27 & 57 & 4899 \\
5 ax & 9893 & 3147 & 294 & 2633 & 17 & 53 & 7190 \\ \hline \hline \end{tabular} \# denotes number of instances.  
\end{table} 
Table 1: Characteristics of datasets produced in this study. Figure 2: Percentage (%) of correctly classified labels by each of seven machine learning (ML) algorithms. the confusion matrix provides a granular assessment of ML algorithms by presenting the number of true and false positives. Table 2 presents confusion matrices for REPTree predictions for the 2n, 2 ax, 5 ac, and 5 ax datasets.