## 3 Research Design
### OTT Consumer Classification with Imbalanced Data  
The first step in our proposed framework is to analyze the traffic of OTT users by using ML to classify them into groups. For consumer classification, data annotation with the help of experts is first needed after collecting the data usage patterns of existing OTT users. In this method, OTT consumer traffic is classified into three types. Consumers with high OTT usage are classified as \"high consumption\", those with a low usage are classified as \"low consumption\", and those with average usage are classified as \"medium consumption\". As discussed, data consumption differs significantly among the three types. According to [PERSON] et al., low consumption users account for 81.3% of the total, while high and medium consumption users account for only a small portion of the total [12]. After collecting OTT user traffic and performing annotation, feature extraction is performed. Features refer to individual and measurable properties of data for ML. The more significant features that are extracted, the higher is the accuracy of the classification. In this study, the public dataset released by [PERSON] et al. was used; thus, our feature sets are also based on their study [12]. As our study's objective is to classify consumers by analyzing patterns of OTT users, it is important to know how much data users have consumed for each OTT application. Therefore, by utilizing the amount of time and data used by consumers for each OTT application as the main features, consumers can be classified based on their usage patterns. A detailed description of the datasets and features used in this study is provided in Section 4.1. After extracting features based on collected consumer data, they are used as training data. Afterward, when incoming consumers' information that needs to be analyzed comes in, features are first extracted. Subsequently, through ML that uses the training data built earlier, the group to which the incoming consumer belongs to is determined. In this process we face the problem of deteriorating classification accuracy due to the class imbalance, as discussed above. In order to improve performance, even in such a situation, we propose a  
Figure 1: Modules of the proposed framework. method to calculate the probability of which class each data sample belongs to based on the cost. To better classify the classes, we use different costs for misclassification errors so that mistakes are eventually minimized [15; 54]. In particular, the OTT consumer dataset consists of three classes and we propose the appropriate formulation and cost matrix. Table 1 shows the asymmetric cost matrix for OTT consumer classes. Misclassification refers to the incorrect classification of users. If a user who belongs to the actual high class is classified as medium or low, the costs that occur in this case are \(C(h,m)\) and \(C(h,l)\), respectively. In the case of OTT consumers, because the number of users belonging to the actual high or medium classes is extremely small compared with those in the actual low class, the classification accuracy for the two classes is inevitably low. Because our goal is to improve classification accuracy for classes having small sample sizes, we need to reduce the number of misclassifications of users who actually belong to the high or medium class. We do this with \(C(h,m)\) and \(C(h,l)\), which are applied to misclassifications of high consumption users, and \(C(m,\ h)\) and \(C(m,l)\), which are applied to misclassifications of medium consumption users. They should be set higher than \(C(l,h)\) and \(C(l,\ m)\), the costs occurring due to misclassifying low consumption users. If the costs are set high, misclassifications will be reduced. On the other hand, if the classification is properly performed, the costs are \(C(h,h)\), \(C(m,m)\), and \(C(l,l)\) for each respective class. Because there is no risk to the classification system in the case of proper classification, the three above costs should all be set to zero.