# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning
### Feature Reduction
#### 2.3.1 Feature Selection  
The feature selection technique has become an indispensable component of the conventional machine learning algorithms (e.g., SVM, decision tree, etc.) [64], and it is one of the simplest ways to reduce data size. It tries to pick a subset of features that \"optimally\" characterize the target variable. Feature selection is the process of selecting the best features in a given initial set of features that yield a better classification performance [65] and regression as well as finding clusters efficiently [66]. In this section, we will focus on feature selection for classification since there is much more work on feature selection for classification than for other ML tasks (e.g., clustering). The best subset contains the least number of dimensions that improve the accuracy the most. It helps us to identify the relevant features (contribute to the identification of the output) and discard the irrelevant ones from the dataset to perform a more focused and faster analysis. The feature selection process consists of four basic steps, which are:  
1. _Subset generation_ is a search procedure that generates candidate feature subsets for evaluation based on a search strategy (i.e., start with no feature or with all features);
2. _Evaluation of subset_ tries to measure the discriminating ability of a feature or a subset to distinguish the target variables;
3. _Stopping criteria_ determines when the feature selection process should stop (i.e., addition or deletion of any feature does not produce a better subset);
4. _Result validation_ tries to test the validity of the selected features. Many evaluation criteria have been proposed in several research works to determine the quality of the candidate subset of the features. Based on their dependency on ML algorithms, evaluation criteria can be categorized into two groups: independent and dependent criteria [16]. For independent measures, we have (1) information measures, (2) consistency measures, (3) correlation measures, (4) and distance measures; for dependent criteria, we have several measures, such as accuracy. Therefore, feature selection can be distinguished into two broad categories, which are _filters_ and _wrappers_, as explained in the following. **Filter Method**: finds the best feature set by using some independent criteria (i.e., information measures) before applying any classification algorithm. Due to the computational efficiency, the filter methods are used to select features from high-dimensional data sets. Additionally, it is categorized as a binary or continuous feature selection method depending on the data type. For example, information gain (IG) can handle both binary and nominal data, but the Pearson correlation coefficient can only handle continuous data. Moreover, it can be further categorized into two groups: feature-weighting algorithms and subset search algorithms, which evaluate the quality of the features individually or through feature subsets. Feature-weighting algorithms give weights to all the features individually and rank them based on their weights. These algorithms can be computationally inexpensive \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** & **Methods** & **Potential Application in SDN** \\ \hline Filter & Low computational cost, fast, scalable & Ignores the interaction with the classifier & CFS, IG, FCBF & QoS prediction [58], traffic classification [59; 60]. \\ \hline Wrapper & Competitive classification accuracy, interaction with the classifier & Slow, expensive for large feature space, risk of over-fitting & Forward/backward direction & Traffic classification [61], QoS prediction [58]. \\ \hline Feature extraction & Reduces dimension without loss of information & No information about the original features & PCA, LDA, AE & Traffic classification [59; 62; 63]. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of dimensionality reduction techniques [16; 57].