# A Survey: Network Feature Measurement Based on Machine Learning
## 4 Future Work
### Future ML in Network Measurement
#### 4.1.5 Discussion on Future ML Methods  
We list the issues that must be solved using the ML approach in this section. Although these issues appear straightforward, they can be gradually adjusted to fit the particular circumstance. A thorough comprehension of the issues that need to be resolved in ML methods will aid researchers in their work, speed up the development of ML in the field of network measurement, and increase the usability and dependability of future ML algorithms. There is yet no established optimum method to resolve this set of issues, even though many academics are researching the application of machine learning to the subject of network measurement. For the time being, the study only focuses on adapting one or more machine learning algorithms to handle certain challenges. The two main challenges for network management in the future will be how to gather a huge amount of high-dimensional data in a real network for analysis and how to create a network management system through network measurement. In general, the future will see a new stage of development in the interaction between machine learning and network measurement. Network measurement will be autonomous and intelligent thanks to the use of machine learning in picture identification, traffic prediction, and other areas. Examples of these capabilities include:  
* Congestion control;
* Automatic extraction of the most important features;
* Network routing;
* Time-series analysis;
* Network layer analysis.  
The ML approach is now the most successful and promising method for addressing network measurement challenges. It is supported by ML framework theory and the data of each study. ML assists decision-makers in deriving knowledge from the data and choosing the best course of action. There is no conclusive evidence that the ML approach is not practical for network measurement and that supervised learning is the most popular method for network measurement, despite the claims of certain academics that the application of ML in network measurement is still insufficient. Because supervised learning makes use of prior experience to forecast results, it aids students in altering their model of prior experience and can offer a precise grasp of the category of objects. Because unsupervised learning has no corresponding output, and the input data are not marked on the algorithm outputs; it cannot produce the desired findings that researchers are hoping to see. Due to its lengthy development process and scant conceptual underpinning, semi-supervised learning is still less common in the field of network measurement. Deep learning models are a black box, even though they are frequently employed in a variety of contexts. It is challenging to comprehend their functions, which makes it challenging to comprehend the input traits that result in these outcomes. This is one reason why deep learning's capacity to address network issues is somewhat constrained. Reinforcement learning and transfer learning perform less well than supervised learning in some applications because of the lengthy sampling period and the fact that they are only relevant to familiar settings.