# Single-Class Data Descriptors for Mapping
## 3 Results and Analysis
### Measuring Performance  
Performance evaluation is crucial in the assessment of a set of SCDDs. For Table 3, the false negative rate (FNR) gives the error on the positive class, while the false positive rate (FPR) shows the error on the negative class. Meanwhile, P is precision, and R denotes recall equivalent with TPR. The statistical metrics can be grouped into (1) a group that the smaller is the better, i.e., the FNR and FPR, and (2) another group that the greater is the better, i.e., P, R, F\({}_{1}\), and AUC. Although it is difficult to describe all SCDDs together, we attempt to make it possible by rating them on a rank table later. Table 3 illustrates that the FNR (e.g., the c5) and FPR (e.g., the c2 and c9) are prominently higher than two known inferior SCDDs (e.g., the c5 and c9), and a slightly inferior one (e.g., c2) is marked as well. Additionally, these unqualified SCDDs are again attention-catching in the second group. For all SCDDs, the precision (e.g., the c2 and c9), recall (e.g., the c5), and F\({}_{1}\) score (e.g., the c2, c5, and c9) support the analysis drawn from the first group, while the AUC error always appears mediocre. However, there are some differences owing to the measuring ability of statistical metrics and the intrinsic characteristics of the diverse SCDDs. The ROC curve (see Figure 8) gives a two-dimensional depiction of the performance of the SCDD. This is due to too many SCDDs with the approximate accuracies so that discriminating the individual ROC curve seems difficult. Thus, we plot them one by one, and the aforementioned inferior SCDDs still can be well reflected. Note that the c5 has the  
Figure 7: Classification results which are shown in false-color style in comparison with the reference result for the “sb”. (**a**) Reference result; (**b**) SGTD; (**c**) RGTD; (**d**) MCDG; (**e**) MoG; (**f**) PDE; (**g**) AENN; (**h**) k-means; (**i**) PCA; (**j**) SOM; (**k**) MST; (**l**) K-NN; (**m**) IncSVDD; (**n**) mean; (**o**) median; and (**p**) vote. lowest operation point, while the c2 and c9 are found in terms of the ROC curve. In addition, the c6 deserves attention. Here, the ROC graphs are not going to be read significantly, as the purpose is achieved. The cost curve (see Figure 9) is a specific performance visualizer using the expected cost, another technique to measure the performance of SCDDs. Each operating point appears as a line in this plot, while the certain one of them is indicated by the dotted line. The combination of operating points that forms the lower hull is indicated by the thick curve and shows the best operating points over the range of costs. Here, the dotted line of c5, and the thick arcs of c2 and c9, again support the performance analysis drawn from the Figure 8. In particular, c6 has a representation that is the same as the ROC graph. Figure 8: ROC curves. (**a**) c1: SGTD; (**b**) c2: RGTD; (**c**) c3: MCDG; (**d**) c4: MoG; (**e**) c5: PDE; (**f**) c6: AENN; (**g**) c7: k-means; (**h**) c9: PCA; (**i**) c10: SOM; (**j**) c11: MST; (**k**) c13: K-NN; (**l**) c17: IncSVDD; (**m**) cmea: meanc; (**n**) cmed; median; and (**o**) cvot votec. The confusion matrix is often applied to visualize the performance of the SCDD by a specific table layout. The OA means overall accuracy, K is the Kappa coefficient, PA denotes the producer's accuracy, and UA represents the user's accuracy. It is somewhat intricate that we structure two kinds of confusion matrices together (see Figure 10), which look similar but not identical. That is the reason  
Figure 9: Cost curves. (**a**) c1: SSTD; (**b**) c2: RGTD; (**c**) c3: MCDG; (**d**) c4: MoG; (**e**) c5: PDE; (**f**) c6: AENN; (**g**) c7: k-means; (**h**) c9: PCA; (**i**) c10: SOM; (**j**) c11: MST; (**k**) c13: K-NN; (**l**) c17: IncSVDD; (**m**) cmea: mean; (**n**) cmed: median; and (**o**) cvot votec.