# Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
## 4 Experimental Results and Analysis
### Experiments of KQI Prediction  
In this section, we evaluate the performance of the proposed KQI prediction algorithm. The training set and the test set are segmented by a sliding window (as shown in Figure 4). That is, under the preset prediction window length \(L\) (unit: day), the training set and the test set are obtained by segmenting the whole dataset according to feature \"Date\". The proposed KQI prediction algorithm is then evaluated with the two datasets. The experiment is repeated by sliding the window throughout the whole dataset. Finally, the mean and variance of the evaluation indices can be obtained. (1) KQI prediction with unweighted ML-kNN  
First, the performance of KQI prediction with the conventional ML-kNN is investigated. In the experiments, the TOP N (N = 1, 4, 7, 10, 13) features are retained to participate in the KQI prediction. The number of nearest neighbors \(k\) in the neighbor searching is taken as 5, 10 and 15 respectively, and \(k\) = 15 gives the best result. The experimental results of KQI prediction under the condition of ML-ReliefF, RF-ML, and UML-Relief  
Figure 4: Segmentation of dataset. Figure 3: Convergence of normalized weights for the three FS algorithms. It can be seen that for UML-ReliefF, TOP 4 features lead to almost the best result, while for ML-ReliefF, TOP 7 features give the best result and for RF-ML all the 13 features should be employed to make the best prediction. In case the prediction with only TOP 1 feature, UML-ReliefF also performs much better than the other two. Therefore, UML-ReliefF is capable of achieving the most reasonable ranking and weights for the features. The KQI prediction results with TOP 7 to 13 features for UML-ReliefF and ML-ReliefF are exactly the same, because the features selected for both the cases are the same, even though the ranking and weights are different. 1. KQI prediction with feature-weighted ML-kNN  
Next, the experiments of KQI prediction with feature-weighted ML-kNN and UM-ReliefF is carried out. The results are presented in Table 4.  
Compared with the results of unweighted prediction in Table 4, it is seen that feature-weighted ML-kNN prediction gives more satisfactory and stable results, since all the features contribute to the prediction according to their weights. Therefore, more the number of features that get involved in the learning, the better performance it will be. On the other hand, for traditional filter feature selection, which does not participate in the learning, one needs to select carefully as to how many features are to be retained for the learning. It should be noted that feature-weighted learning is adequate for the case of not too many features. For high-dimensional dataset, we still need to balance the learning performance and the computational complexity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Features** & **Hamming Loss** & **Macro-F1** & **Micro-F1** & **Instance-F1** \\ \hline
1 & 0.187 \(\pm\) 0.017 & 0.876 \(\pm\) 0.006 & 0.878 \(\pm\) 0.008 & 0.863 \(\pm\) 0.012 \\
4 & 0.166 \(\pm\) 0.003 & 0.885 \(\pm\) 0.004 & 0.888 \(\pm\) 0.003 & 0.875 \(\pm\) 0.003 \\
7 & 0.167 \(\pm\) 0.004 & 0.883 \(\pm\) 0.004 & 0.887 \(\pm\) 0.003 & 0.873 \(\pm\) 0.003 \\
10 & 0.163 \(\pm\) 0.005 & 0.887 \(\pm\) 0.004 & 0.890 \(\pm\) 0.003 & 0.876 \(\pm\) 0.004 \\
13 & 0.159 \(\pm\) 0.002 & 0.888 \(\pm\) 0.004 & 0.892 \(\pm\) 0.003 & 0.878 \(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of KQI prediction with feature-weighted ML-kNN (\(k\) = 15, \(L\) = 14) and UML-ReliefF.