# Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
## 5 Conclusions  
Currently, mobile network maintenance relies heavily on manual investigation and customer feedback. It is incapable of finding service deterioration in a timely manner and, thus, engineers cannot take prompt actions to solve it before it is too late. There is some research of service quality prediction reported in the area of web service recommendation [3; 10; 11]. Most of them are designed for low dimensional data and assume that the feature and label space is ideally independent. There is limited research of OTT service prediction for high-dimensional real data in mobile networks. In view of this, this paper tries to employ feature selection and multi-label learning in the prediction of OTT service quality, which would be highly valued information for engineers.  
Based on the mobile crowdsensing data acquired from massive smartphone users, we have first investigated the application of multi-label Relieff in the feature selection and proposed an improved ML-Relieff, namely, UML-Relieff. Then, a feature-weighted ML-kNN algorithm was proposed for KQI prediction, by combining UML-Relieff and ML-kNN. Unlike the traditional filter FS, which does not participate in the learning process that follows, here the weights of the selected features are utilized in the prediction. We call it embedded filter feature selection.  
Taking OTT web browsing service as an example, experimental results of KQI prediction show that UML-Relieff is able to overcome the drawback of ML-Relieff, which does not consider the relations among the labels. Second, it is also proved that feature-weighted KQI prediction, which takes full advantage of all the features, yields better performance than its unweighted counterpart. This is especially meaningful for the case, where the dimension of the features is not so high and computational complexity is tolerable.  
The proposed method of this paper provides a promising way for the mobile network operation engineers to tackle service deterioration as early as possible, and thus may improve the intelligence of mobile network operations.  
In the future, we will further extend the KQI prediction into regression problem. That is, not just predicting whether the service quality will be \"good\" or \"bad\", but finding the actual KQI values instead. Another challenge that deserves further investigation is as to how to utilize the soft information embedded in the original KQI labels before hard decision. It is likely to improve the KQI prediction,  
Figure 5: Comparison of KQI prediction performance under different settings of window length.  
if we can take advantage of it in a proper way. In addition, the impact of distance measures on the performance of nearest neighbour searching in the KQI prediction will also be investigated.  
Conceptualization, K.L. and W.L.; methodology, K.L. and S.X.; validation, S.X. and M.X.; visualization, S.X.; writing, K.L. and S.X.; data curation, K.L. and W.L.; funding acquisition, M.X. and K.L.; writing-review & editing, L.Z., K.L. and S.X. All authors have read and agree to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (61841601, 61972040), the Collaborative Innovation Project of Chaoyang District of Beijing (CYXC1805), the Premium Funding Project for Academic Human Resources Development in Beijing Union University (BPHR2018 CZ05), and Beijing Municipal Education Commission Science and Technology Project (KM201911417010).  
The authors would like to thank [PERSON] of Concordia University, Canada, for going through the paper and making a number of suggestions, which has helped in improving the presentation of the paper.  
The authors declare no conflict of interest.