# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning
### Feature Reduction  
The success of conventional ML algorithms generally depends on the features to which they are applied. In other words, their performance depends not only on the parameters but also on the features, which are used to describe each object to the ML models [50]. In this section, we provide an overview of basic concepts as well as the motivation of the dimensionality reduction technique.  
In general, the dataset can contain irrelevant (a feature that provides no useful information) and redundant (a feature with predictive ability covered by another) features, which cause an extra computational cost for both storage and processing in an SDN environment and decreases the model's performance [51; 52]. For example, the time complexity of many ML algorithms is polynomial in the number of dimensions; for example, the time complexity of logistic regression is \(O(mn^{2}+n^{3})\), where \(n\) represents the number of features and \(m\) represents the number of instances [53]. These challenges are referred to as the _curse of dimensionality_, which is one of the most important challenges in ML. The curse of dimensionality was introduced by [PERSON] in 1961 [18].  
In addition, each model is only as good as the given features. Additionally, the model accuracy depends not only on the quality of input data but also on the amount features. The model may perform poorly and can overfit if the number of features is larger than the number of observations [54]. Moreover, the low-latency applications (e.g., anomaly detection) require fast analysis, especially in the context of the fifth-generation (5G) networks, and feature reduction can help to leverage heavy analysis at network levels. All these make feature reduction difficult tasks not only because of the higher size of the initial dataset but because it needs to meet two challenges, which are to maximize the learning capacity and to reduce the computational cost and delay by reducing the number of features.  
In fact, there are many benefits of feature reduction techniques, such as (i) improving the performance of learning algorithms either in terms of learning speed or generalization capacity and (ii) reducing the storage requirement. It reduces the original data and retains as much information as possible that is valuable for the learning process. Therefore, in a dataset with a high number of features, the data reduction process is a must to produce accurate ML models in a reasonable time. It can be divided into feature selection (i.e feature elimination) and feature extraction, as shown in Figure 3. The main difference is that feature selection selects a subset of original features, while feature extraction creates new features from the original ones. Table 7 presents the advantages and disadvantages of each approach.  
Each approach can be used in isolation or in a combination way with the aim of improving performance, such as the accuracy, visualization, and comprehensibility of learned knowledge [55]. [PERSON] et al. [56] proposed bi-level dimensionality reduction methods that combine feature selection methods and feature extraction methods to improve classification performance. Then, they compared the performance of three-dimensionality reduction techniques with three datasets. Based on their results, these combinations improve the precision, recall, and F-measure of the classifier. Additionally, we can find feature construction, which is different from the feature reduction technique. It is one of the  
Figure 3: The classification of dimensionality reduction approaches.  
preprocessing tasks that tries to increase the expressive power of initial features when the initial features set do not help us classify our data well enough. It is the process of creating additional features instead of reducing the features from the initial features set, beginning with \(n\) features \(f_{1},f_{2},f_{3},\ldots,f_{n}\). After feature construction, we will have new \(p\) features \(f_{n+1},f_{n+2},f_{n+3},\ldots,f_{n+p}\).