## 4 Results and Discussion
### Time-Aware Consumer Classification  
To reduce the time required for deep learning, we first classified high- and low-consumption data by using machine learning and MetaCost. We then classified only the remaining ambiguous data using deep learning. KNN and J48 decision trees were the machine learning methods used as the primary filter. Although KNN showed the best performance among all machine-learning methods, J48 achieved fast and highly accurate results. The cost requirement of applying MetaCost is defined as \"the cost incurred when classifying data other than high/low consumption as high or low consumption\". Therefore, with the cost set to \"high\", ambiguous data are forwarded to the secondary deep-learning classification. This experiment was performed while changing the cost from 1 to 30 in steps of five. If the cost was one, the weight for all errors was one. Accordingly, the result obtained was the same as that without the application of MetaCost. If the cost was set higher than one, classification was performed to reduce costs. At each step as the cost approached 30, no significant difference was observed from the previous step. Thus, to observe the most conspicuous difference, the experiment was conducted with the cost set to 30. In the secondary classification process, the MLP algorithm was applied for deep learning. Table 5 shows the processing results of the primary filter using KNN and J48 while adjusting the cost from 1 to 30. The table presents the number of data filtered by the primary filter, incorrectly classified by the filter, and processed by deep learning (the secondary classification) with the final detection time. With an increase in the cost setting, only the more obvious data were filtered out. Thus, the number of filtered data decreased, and those processed through deep learning increased, resulting in an increase in detection time. However, even if the cost was set to an extremely high value of 30, the detection time was about half. This resulted in the best accuracy while reducing the detection time by more than half. If the cost was set to \"low\", the detection time was reduced to approximately 23%. However, in this case, the number of incorrect classifications increased, negatively affecting the classification accuracy of the entire framework. When using KNN as the primary filter, results showed fewer errors. However, the number of filtered data was less than that when using J48. Therefore, KNN was determined to utilize more time than J48. On the contrary, although J48 utilized less time because of more filtering, it resulted in more errors. Therefore, the overall classification result of J48 was poor. Table 6 summarizes the overall classification accuracy of the framework per filtering method. Because of space limitations, the detailed results are included in the Appendix A. As shown in the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Classified as**} & \\ \hline
**Real Data** & \begin{tabular}{c} **High** \\ **Consumption** \\ \end{tabular} & \
\begin{tabular}{c} **Medium** \\ **Consumption** \\ \end{tabular} &
\begin{tabular}{c} **Low** \\ **Consumption** \\ \end{tabular} & **Recall** & **Precision** & **F-Measure** \\ \hline High Consumption & **98.91\%** & 1.09\% & 0\% & 0.989 & 0.974 & 0.981 \\ Medium Consumption & 3.46\% & **94.60\%** & 1.94\% & 0.946 & 0.973 & 0.959 \\ Low Consumption & 0.21\% & 1.05\% & **98.74\%** & 0.987 & 0.981 & 0.984 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detailed classification results of CNN.