# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning
### Conventional Machine Learning Models
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline
**Method** & **Description** & **Strengths** & **Weaknesses** \\ \hline \multirow{4}{*}{Decision tree (DT)} & Decision tree is a tree-like structure, where every leaf (terminal) corresponds to a class label and each internal node corresponds to an attribute. The node at the top of the tree is called the root node. Tree splitting uses Gini Index or Information Gain methods [46]. & Simple to understand and interpret, requires little data preparation, handles many types of data (numeric, categorical), easily processedata with high dimension & Generates a complex tree with numeric data, requires large storage \\ \hline \multirow{4}{*}{Random forest (RF)} & Random forest was developed nearly 20 years ago [47] and is one of the most popular supervised machine learning algorithms that is capable to be used for regression and classification. As their name would suggest, random forests are constructed from decision trees. It uses the bagging method, which enhances the performance. & Efficient against over-fitting real-time applications \\ \hline \multirow{4}{*}{Support vector machine (SVM)} & SVM is a powerful classification algorithm which can be used for both regression and classification problems. However, it is mostly used as classification technique. It was initially developed for binary classification, but it could be efficiently extended to multiclass problems. It can be a linear and non-linear classifier by creating a splitting hyperplane in original input space to separate the data points. & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } \\ \cline{1-1}  & KNN is a supervised model reason with the underlying principal “Iell me who are your friends, 1 will tell you who are you” [48]. It classifies new instances using the information provided by the K nearest neighbors, so that the assigned class will be the most common among them (majority vote). Additionally, as it does not build a model and no work is done until an unlabeled data pattern arrives, it is thus considered as a lazy approach. & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } \\ \cline{1-1}  & K-means is a well-known unsupervised model. It can partition the data into K clusters based on a similarity measure, and the observations belonging to the same cluster have high similarity as compared to those of other clusters. & \multirow{4}{*}{Fast, simple and less complex} & Requires large storage space, determining the optimal value of K is time consuming, K \\ \cline{1-1}  & The main idea of boosting is to improve the performance of any model, even weak learners (i.e., XGBoost, & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } \\ \cline{1-1} Boosting algorithms & AdaBoost, etc). The base model generates a weak prediction rule, and after several rounds, the boosting algorithms improve the prediction performance by combining the weak rules into a single prediction rule [49]. & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{1-1}  & & \\ \end{tabular}
\end{table} 
Table 6: Comparison of classical ML approaches used in SDN.