## 3 Research Design
### Appling Machine- and Deep- Learning to OTT Consumer Classification
#### 3.1.1 Conventional Machine Learning Methods  
**The KNN** is a typical classification method that applies clustering. It first confirms which class the \(k\) neighbors of a data point belong to, and it then performs classification by taking the majority vote based on the result. If \(k=1\), it is assigned to the nearest-neighbor class. Therefore, if \(k\) represents an odd number, classification becomes easier, because there is no possibility of a tie [44]. A neighbor in KNN can be defined by calculating the distance between vectors in a multidimensional feature set. The distance between vectors is calculated using Euclidean and Manhattan distances. Suppose that  
Figure 1: Machine- and deep-learning-based over-the-top (OTT) consumer classification process. input-sample \(x\) has \(m\) features. The feature set of \(x\) is expressed as \((x_{1},\ x_{2},\ \cdots,\ x_{m})\), and the Euclidean and Manhattan distances of \(x\) and \(y\) are defined as follows [45]:  
\[\text{Euclidean}:\ D(x,y)=\sqrt{\sum\
olimits_{i=1}^{m}\left|x_{i}-y_{i} \right|^{2}}, \tag{1}\]  
\[\text{Manhattan}:\ D(x,y)=\sum\
olimits_{i=1}^{m}\left|x_{i}-y_{i}\right|. \tag{2}\]  
The greatest advantage of the KNN is its straightforwardness, and the variables are not required to be adjusted. This method only requires the assignment of a \(k\) value. However, if the distribution is distorted, KNN cannot be applied, because the data may not belong to the same class, even if it is close to its neighbors. Additionally, when dealing with multidimensional data having many features, classification accuracy may be degraded, owing to the curse of dimensionality. Thus, it is essential to reduce features [46]. **Decision trees** are built upon the tree model and re-used to classify an entire dataset into several subgroups. When traversing from an upper to a lower node, nodes are split according to the classification variables. Furthermore, nodes of the same branch have similar attributes, whereas nodes of different branches have different attributes. The most typical decision-tree algorithms are ID3 and C4.5. The C4.5 algorithm, derived from ID3, minimizes the entropy sum of the subsets by leveraging the concept of \"information gain\". The subset is split to the direction that maximizes information gain. Thus, accuracy is high when classification is performed through the learned result. Decision trees have the advantages of intuitiveness, high classification accuracy, and simple implementation. Therefore, they are widely adopted for various classification tasks. However, for data containing variables at different levels, the level is biased mainly to most of the data. Moreover, for a small tree with fewer branches, rule extraction is easy and intuitive, but accuracy may decrease. Moreover, for a deep and wide tree having many branches, rule extraction is difficult and non-intuitive, but accuracy may be higher than that of a small tree. **The SVM** performs classification based on a hyperplane that separates two classes in the feature space. The hyperplane having the longest distance between the closest data points to the hyperplane in two classes is set to have the maximal margin. For inputs \(x\) and \(y\), the hyperplane separating the classes is defined as \(w^{T}x\ +\ b\ =\ 0\). After finding the distance between the hyperplane and closest data points of two classes, the optimization equation for maximization is defined as follows:  
\[\min_{w,\ b}\ \Phi(w)=\frac{1}{2}\|w\|^{2}, \tag{3}\]  
where variables \(w\) and \(b\), which satisfy the following convex quadratic programming, become variables that build the optimal hyperplane [45]:  
\[\text{s.t}\ y_{i}\Big{(}\text{w}^{T}x_{i}+b\Big{)}\geq 1,\ i=1,\cdots,\ l. \tag{4}\]  
The SVM achieves high performance for a variety of problems. It is also known to be effective for the case of many features. Although SVM solves binary-class problems and can be applied to multiclass problems having various classes, it must solve multiple binary-class problems to derive accurate results.