# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
## 4 Discussion
In contrast, related works used different approaches for predicting the perceived quality of video conferencing. These include the binary classification of quality as \"acceptable\" or \"unacceptable,\" individual binary classifications for video and audio communications, and ternary classifications provided by applications. However, each of these alternative approaches is limited. For example, assessment of the perceived quality of video conferencing into acceptable and unacceptable binary classes may conceal the details required for identifying the root causes of quality degradation and for planning improvements. Representing the assessment of perceived quality in the form of MOS values addresses these issues and better classifies issues according to predefined levels in service providers' business and operational plans. Most of the off-the-shelf Wi-Fi APs use a Linux-based operating system, which provides 802.11 network performance parameters in the form of counters and information variables. However, some of these parameters may not correlate with PQoS, and APs may not update some of the parameters, so it is reasonable to eliminate unnecessary parameters. Unnecessary parameters may cause ML algorithms to overfit or underfit training data and consequently do not generalize well to unseen events. Therefore, feature engineering techniques can be used to manipulate features in datasets and improve the precision of the produced model. This study considered two criteria for comparing feature selection processes. The first measured accuracy, and the second measured whether selected features were informative enough to infer the root cause of any quality degradation. Thus, after applying various feature selection evaluators and search algorithms to datasets, we selected the CfsSubset with PSOSearch as the best feature selection technique. The best-first search algorithm with backward direction selected the fewest features in the datasets and could not compete with the accuracy of the full-featured dataset. Although both the IWSSEmbeddedNB and PSOSearch achieved similar accuracy as--or even higher accuracy than--the full-featured dataset, the number of features selected by the PSOSearch was greater than the number selected by the IWSSEEmbeddedNB, and the new dataset presented more informative features, which could facilitate identifying the root causes of any quality degradation. Recognizing the challenge of choosing a suitable ML algorithm, this study proposed three criteria--accuracy, interpretability, and computational cost--to select the best algorithm that is executable on off-the-shelf Wi-Fi APs. Although the classification accuracies of all six algorithms were quite similar, this study selected REPTree as the best ML algorithm for this use case. Most of the related works exclusively utilized ML evaluation metrics, such as accuracy, recall, precision, F-measure, ROC, and RMSE, to assess the performance of their ML approaches. However, these ML evaluation metrics only consider how accurately the ML algorithms can classify the input data, and the model with the best precision is not always the best algorithm to implement. Therefore, this study considered interpretability and computational cost criteria to ensure the implementation and usefulness of the proposed approach for ISPs. The REPTree is a fast learner algorithm that produces a decision tree using information gain and prunes the tree using reduced error pruning. Although J48 (C4.5), LMT, and NBTree achieved similar classification accuracies and satisfied the interpretability criterion, REPTree was fast enough to produce the model within the expected interpretability level and with similar accuracy. Furthermore, meta-algorithms such as AdaBoost can enhance the classification of REPTree learner even further. The MLP and NBTree were CPU-demanding algorithms compared with the other four candidate algorithms, whereas the rule-based RIDOR produced rules that were not easy to interpret when compared with those of a tree structure.