## 5 Results of QoE\({}_{i}\) Modeling and Discussion
### Models for \(QoE_{i}\) Classification  
Given that the \(QoE_{i}\) values obtained on the basis of the mathematical assessment model presented by Expression (5) are of a continuous nature, in the first step of the process of creating this type of model it is necessary to map a continuous into a discrete _absolute category rating_ (ACR) scale as presented in Table 7 [64; 65]. Structured input-output vectors for training and testing are processed in the _Auto Classifier_ node in order to automatically create different models for \(QoE_{i}\) classification simultaneously. Similar to the _Auto Numeric_ node, in only one pass through the modeling process, _Auto Classifier_ examines different machine learning techniques with default option settings. Options in this case mean the number of neural network layers, the number of neurons in each layer, the shape and parameters of the classification function, the training algorithm, stopping criteria, tree size, etc. Based on the test results, _Auto Classifier_ offers the most accurate solutions and ranks them according to the overall classification accuracy expressed in percentages. Based on the available set of 157 vectors, several models were created, and Table 8 shows the results of testing the three most accurate models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model/Component** & **Prediction Accuracy** & **Number of Inputs** & **Number of Nodes** \\
**Number** & **(A)** & **Number of Inputs** & **Number of Nodes** \\ \hline
1 & 69.7\% & 9 & 23 \\
2 & 52.3\% & 9 & 19 \\
3 & 47.0\% & 10 & 17 \\
4 & 39.2\% & 10 & 25 \\
8 & 34.6\% & 10 & 35 \\
5 & 30.4\% & 10 & 25 \\
9 & 27.8\% & 10 & 29 \\
6 & 25.5\% & 10 & 29 \\
10 & 13.7\% & 10 & 25 \\
7 & 10.0\% & 10 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prediction results for ensemble components of the C&R tree model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Continuous Scale** & **ACR Scale** \\ \hline \(0\leq QoE_{i}<0.5\) & 0 \\ \(0.5\leq QoE_{i}<1.5\) & 1 \\ \(1.5\leq QoE_{i}<2.5\) & 2 \\ \(2.5\leq QoE_{i}<3.5\) & 3 \\ \(3.5\leq QoE_{i}<4.5\) & 4 \\ \(4.5\leq QoE_{i}\leq 5\) & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mapping of continuous \(QoE_{i}\) values into the ACR scale. Based on the results shown in Table 8, it is concluded that the k-NN model has the highest classification accuracy of all tested models, with 50% correctly classified input vectors. In order to increase the accuracy of the classification, with the DA method, the basic dataset is expanded with synthetic copies in the same way as it is presented in the \(QuE_{i}\) prediction model. Four iterations of increasing the dataset were performed, according to the procedure previously carried out for predictive models (Figure 8), and the results of testing the three most accurate models after each iteration are presented in a diagram in Figure 11. As a criterion for selecting the final, most accurate model, the maximum total classification accuracy was observed, which was achieved after the fourth iteration, i.e., with a total set of 2512 input-output vectors, and it amounts to 94.048%. Increasing the overall classification accuracy by increasing the data set can be represented by a linear growth trend that has the following form:  
\[OverallAccuracy=2.009\cdot c+71.487 \tag{10}\]  
where \(c\) represents the serial number of the classification model starting from the C&R Tree model with serial number 1 in the first iteration. SVM represents one of the most powerful prediction methods, which is based on statistical learning theory and the VC (Vapnik-Chervonenkis) theory proposed by [PERSON] (1982, 1995) and [PERSON] (1974). This machine learning technique belongs to the supervised learning paradigm; therefore, in the training dataset, there must be a label of one of the two categories to which each input vector belongs. The SVM training algorithm creates a model that assigns new examples to one of the categories, making it a non-probabilistic binary linear classifier.