## 3 Research Design
### Time-Aware Consumer Classification Based on MetaCost and Deep Learning  
In this study, we classify consumers into three consumption types (i.e., high, low, and average) by analyzing their OTT-usage traffic. Notably, there are two other classes of users that we ignore: Those that use an extremely heavy amount of OTT services and those who rarely use services. As these classes, which have extreme characteristics, can be easily classified via general machine learning, deep learning does not need to be used to classify these extrema. Therefore, we propose a framework that first filters high- and low-consumption consumers through a fast and relatively accurate machine-learning technique. Then, it performs deep-learning-based classification for the remaining customers. This framework shortens the overall computation time by reducing the number of samples to which deep learning is applied, allowing it to focus on the more ambiguous classes [41]. Figure 4 illustrates the proposed time-aware framework based on MetaCost.  
Figure 3: Convolutional neural network (CNN) process.  
Figure 2: Multilayer perceptron (MLP) process.  
Figure 5 shows a simple schematic of the high- and low-consumption filtering process, which forwards the non-extrema data to the deep-learning-based classifier. By setting the cost of errors that misclassify non-high-consumption data as \"high\" greater than that of errors that incorrectly classify high-consumption data as \"medium\" or \"low\", we prevent other classes of data from being misclassified as \"definite high-consumption\". When classifying definite low-consumption data, we filter out only the obvious data by setting the cost with the contrapositive logic. If the cost is set high in order to not mix the filtered data with other data, all data that are slightly ambiguous are forwarded to the next step, as shown with the \"high-cost\" process of Figure 5. As a result, the load on deep learning is mitigated. However, there is an increased chance of lower accuracy, because, during the filtering process, medium-consumption data can be misclassified as high- and low-consumption data. We adjust the tradeoff relationship between accuracy and time-consumption by controlling costs according to the number of data and resource state.  
In the case of general machine learning, the weights for the errors resulting from the classification process remain the same. The MetaCost method sets the cost of errors differently and is suitable to be applied to the proposed filtering framework, because classification is performed in terms of minimizing costs. The MetaCost method assigns each data to a class satisfying the following equation:  
\[x^{\prime}s\text{ class}=\text{ arg min}_{i}\sum\
olimits_{j}p(j\big{|}x)\;C(i,j), \tag{6}\]  
where \(p(j\big{|}x)\) is the probability that \(x\) belongs to class \(j\), and \(C(i,j)\) is the cost incurred when \(x\) actually belongs to class \(j\) but is classified as class \(i\). After calculating the cost of misclassification for each datum, it is assigned to the class having the lowest cost [41].  
The proposed framework's classification time is far less than that of the simple deep-learning method, but deviations can occur based on the cost setting. However, deep learning is generally applied after filtering more than half of the data, making it advantageous over the proposed framework in terms  
Figure 4: Proposed framework.  
Figure 5: Filtering results according to cost.  
of time consumption. Thus, even with greatly increased sizes of the analysis dataset, the strengths of proposed framework will stand out. As mentioned, the framework increases in flexibility via cost-setting adjustments. If the cost is properly adjusted according to the environment in which this framework is adopted, classification can be performed according to the time and accuracy desired by the analyst.