## 5 Anomaly Detection Model Test Results
### One-Class SVM  
Table 12 shows the training results of the models per the hyperparameters configured as shown in Table 8. The results of one-class SVM-m2 through m5 show that both TPRs and TNRs are higher than one-class SVM-m1. Through this, we can show that, for one-class SVM, it is more appropriate to construct normal/anomaly per data instance as the output data. As for the discretization of continuous signals, the comparison between one-class SVM-m2 and one-class SVM-m3 through m5 shows that dense discretization is more advantageous for TPR. One-class SVM-m3 through m5 are the results of different outlier fractions, and we can see that the larger the outlier fraction, the higher the TPR and the lower the TNR. This is because the larger the outlier fraction, the narrower the decision boundary, as shown in Figure 17. The decision boundary is the criterion by which the model distinguishes anomalies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **TPR [\%]** & **TNR [\%]** & **Accuracy [\%]** \\ \hline One-class SVM-m1 & 56.0 & 83.5 & 66.3 \\ One-class SVM-m2 & 93.3 & 99.9 & 97.9 \\ One-class SVM-m3 & 96.8 & 100 & 99.0 \\ One-class SVM-m4 & 97.2 & 99.9 & 99.0 \\ One-class SVM-m5 & 98.2 & 99.0 & 98.7 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Test results of the one-class SVM-based anomaly detection models.  
Figure 17: Decision boundaries of the one-class SVM-based anomaly detection models based on the outlier fraction: (a) outlier fraction = 0.01 [%]: (b) outlier fraction = 0.1 [%]: (c) outlier fraction = 1 [%].  
The training results of each architecture and model's configuration are summarized as follows. First, for MLP, it is possible to derive the model that adequately distinguishes normal/anomaly, but we could see the phenomenon where local optimal models with low TNRs are easily derived. The LSTM and CNN-based models also showed low TNRs. Like this, the TNRs of MLP, LSTM, and CNN all tended to have low values. This is thought to be because anomalous data have fewer numbers than normal data. Training is conducted in the direction of increasing the accuracy of the training dataset. Therefore, even if the accuracy of the class having a low number is low, if the accuracy of the class having a high number is high, the overall accuracy is increased. Since our training data also have more normal data than anomalous data, we can infer that the models were trained in this way to increase the accuracy of normal data. This can be confirmed from the high TPR results and low TNR results for each architecture. In the case of one-class SVM, the models that configured normal/anomaly per data instance as output showed high TPRs and TNRs. Through these results, we found that for engine clutch engagement/disengagement data with an imbalance in normal/anomaly, constructing the training architecture to determine normal/anomaly by data instance and performing one-class classification are advantageous for anomaly detection. In this work, we used one-class SVM, which is most commonly used for one-class classification, but other one-class classification architectures are also expected to show high anomaly detection performance.