## 2 Literature Review
### Problem Statement  
#### 2.3.1 Class-Imbalance Problem  
There are diverse demographics of consumers that consume OTT services. Gen-Z's (born between 1997 and 2006) and Millennials' (born between 1983 and 1996) lives have included breakthrough technologies and cultural changes that now include OTT services. Statistics show that the Millennials and Gen-Zs use 17 and 14 subscription services each, respectively, whereas Baby-Boomers subscribe to eight on average [16]. A similar phenomenon was observed in network usage statistics. Seventy percent of Gen-Zs subscribe to Netflix; however, as age increases, the subscription rate decreases, with only 39% of Baby-Boomers subscribing to it [46]. When consumers are grouped by country, the characteristics differ among groups again. Consumers in populous India spend more than 45B h using video streaming services, more than double that of US consumers. The country having the highest video content per capita is South Korea, where almost 2000 h are spent per person [47].  
As such, OTT market consumers can be divided into various groups, and the number of group members varies widely. According to [PERSON] et al. [14], who analyzed the data traffic of mobile users (including OTT), heavy users who comprise only 2-4% of the total consume the most data. They collected mobile traffic from Finland, Germany, the UK, Japan, and Brazil and classified users using a clustering method. In Finland, which had the largest number of research samples, the proportions of heavy, regular, and light users were 3.5, 41.9, and 54.6%, respectively, and the data used for uploads and downloads by each group comprised 328.7, 64.3, and 9.4 MB, respectively. Heavy users, comprising only about 3% of the total, used more data than approximately 97% of the other users. A similar phenomenon was found in other countries, although the proportions were different [14]. Furthermore, similar trends were found in a study that grouped users by analyzing actual OTT traffic. According to [PERSON] et al., the high, medium, and low consumption groups consisted of 84, 50, and 582 persons, respectively [12].  
Similar to the case of OTT user groups, very small or very large numbers of samples for particular classes are often observed in real-world environments [48]. Hence, analysis results will likely be biased toward the most populous classes [49]. With ML, this problem is called the \"class-imbalance problem\" and is viewed as one of ten major problems to overcome [50]. As reviewed above, very few studies have analyzed OTT traffic, and some did not consider the class-imbalance problem at all, which is easily seen in the OTT demographics. If this problem is not considered, performance deterioration problems will occur with real-world grouping and pricing strategy computations. Therefore, we propose a method to overcome such problems for ML methods, as applied to an actual OTT environment.  
In areas other than OTT, the class-imbalance problem is often encountered when classification is performed. For example, many studies on the detection of Twitter spam have overlooked this problem. However, other studies applied methods of artificially resampling the data to solve this problem [51; 52]. Nevertheless, if re-sampling is performed based on a small amount of data, there will be the problem that the minor classes have the characteristics of only a small amount of collected data. To overcome this, cost-based methods have been applied in the binary classification field [15; 53]. These adjust the cost of misclassifications to reduce them for the smaller classes. While employing a cost-based approach in this study, we leverage a method that can be applied to a dataset comprising many classes in accordance with the characteristics of OTT users.