.40      **87.41**     368.97       12.60        94.20        22.23       370.12
   **SDD-E Dynamic**      18.53        81.80        30.21      6808.58       20.28      **71.40**      31.58      8985.92       25.90        97.40        40.92      5881.37       13.44        97.20        23.61      5747.19
   **SDD-E Dynamic+**     27.85        25.60        26.68      8019.23       26.19        56.40        35.77      9197.94       77.60        79.40        78.49      6408.12     **50.55**      84.00      **63.11**    6176.57
        **MGoF**          10.94         4.40        6.28      **347.97**     10.08        51.60        16.87       571.93        6.40        13.60        8.70        440.26        2.75        11.60        4.45        509.58
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[2]{} ![image](./F1OnDivergenceMetric.pdf){width="\linewidth"} \[fig:divergence-metric-f1\] ![image](./ROCUnderDifferentMetrics.pdf){width=".8\linewidth"} \[fig:divergence-metric-roc\]

The last experiment was carried out on random-shaped distribution data set, with $alpha=0.1$ and rest parameters the same. Under different divergence metrics mentioned in section \[sec:preliminaries\], F1 scores were calculated among all SDD algorithms and ROC curves were recorded on SDD-R and static SDD-E. Given that MGoF was defined specifically on Kullback-Leibler divergence, it cannot be tested in the same way. Results are shown in Fig. \[fig:divergence-metric-f1\] and Fig. \[fig:divergence-metric-roc\].

It is indicated that Jensen-Shannon divergence is suited to all techniques due to its symmetry. Kullback-Leibler divergence provides more evident differences when references were given. Bhattacharyya distance and Hellinger distance turned out almost as good as Jensen-Shannon divergence, but they consumed less time. Kolmogorov-Smirnov Statistic performed relativ