# A Survey: Network Feature Measurement Based on Machine Learning
## 5 Conclusions  
In this paper, the ML algorithm and its practical use in network measurement are discussed about the viability of using the ML approach to network measurement. This article introduces various ML algorithms used for network monitoring from the standpoint of classifying ML methods. It concentrates on using ML algorithms for network measurement, though. Most academics prefer supervised learning over other learning techniques, including unsupervised learning, semi-supervised learning, reinforcement learning, and migration learning, because of its high accuracy and the fact that the majority of datasets include labeled samples. Currently, supervised learning will continue to be a key component of future machine learning algorithms used in network monitoring. The challenges and potential possibilities for ML algorithms development in the area of network measurement are also noted. Due to the lesser complexity and expense of dealing with available bandwidth compared to some other network characteristics, the majority of research directions in the application of network measurement concentrate on this factor. Furthermore, the challenges that the ML algorithm faced when measuring networks are identified, as well as potential future study areas. The complexity of the model and the limits of the training data make it challenging to find the most efficient ML method, which is why the ML algorithm occasionally performs below expectations, even though it has made enormous strides in the field of network measurement. As network measurement using the ML method plays a significant role in future network construction and network performance improvement, including congestion control, resource management, and real-time monitoring, it is hoped that the discussion in this paper can offer straightforward assistance for scholars in related fields to apply the ML algorithm to network measurement. This will help researchers in different fields master some basic problems. The main issues raised by this study will be thoroughly investigated in the future.  
M.S., conceptualization, methodology, software, formal analysis, investigation, writing--original draft; B.H., investigation; R.L., investigation; J.L., supervision; X.Z., supervision; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 92067108, the Shandong Provincial Natural Science Foundation of China under Grant No. ZR2020 MF057, and the Beijing Nova Program of Science and Technology under grant Z19110000119113.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANN & Artificial Neural Network \\ SVM & Support Vector Machine \\ DNN & Deep Neural Network \\ DT & Decision Tree \\ FNN & Feedforward Neural Network \\ RNN & Recursive Neural Network \\ LSTM & Long Short-Term Memory \\ CNN & Convolutional Neural Networks \\ RBF & Radial Basis Function \\ XGBoost & eXtreme Gradient Boosting \\ LR & Logistic Regression \\ SVR & Support Vector Regression \\ RF & Random Forest \\ MLP NN & Multilayer Perceptron Neural Networks \\ SSL & Semi-Supervised Learning \\ \end{tabular}