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Table 3
Global accuracy (AC), precision, recall, and F1 metrics with their standard deviations σ (in brackets) obtained with different algorithms using either ALHAMBRA or SDSS colours.
| Database | Classifier | ΑC(σ) | Class | Precision (σ) | Recall (σ) | F1 (σ) |
|---|---|---|---|---|---|---|
| ALHAMBRA | KNN | 0.840 (0.025) | QSO | 0.869 (0.073) | 0.708 (0.091) | 0.774 (0.043) |
| GAL | 0.773 (0.065) | 0.858 (0.060) | 0.810 (0.035) | |||
| STA | 0.893 (0.061) | 0.962 (0.025) | 0.925 (0.031) | |||
| ALHAMBRA | GBoost | 0.864 (0.020) | QSO | 0.845 (0.043) | 0.801 (0.039) | 0.821 (0.025) |
| GAL | 0.820 (0.058) | 0.839 (0.044) | 0.828 (0.030) | |||
| STA | 0.923 (0.053) | 0.954 (0.023) | 0.937 (0.027) | |||
| ALHAMBRA | SVC | 0.828 (0.018) | QSO | 0.797 (0.061) | 0.733 (0.056) | 0.761 (0.031) |
| GAL | 0.789 (0.068) | 0.801 (0.049) | 0.792 (0.041) | |||
| STA | 0.892 (0.054) | 0.954 (0.023) | 0.921 (0.025) | |||
| ALHAMBRA | FNN | 0.838 (0.014) | QSO | 0.690 (0.040) | 0.440 (0.050) | 0.540 (0.030) |
| GAL | 0.860 (0.020) | 0.914 (0.008) | 0.884 (0.011) | |||
| STA | 0.859 (0.018) | 0.930 (0.030) | 0.892 (0.013) | |||
| SDSS | KNN | 0.784 (0.020) | QSO | 0.709 (0.054) | 0.701 (0.038) | 0.704 (0.037) |
| GAL | 0.722 (0.046) | 0.709 (0.039) | 0.714 (0.029) | |||
| STA | 0.912 (0.046) | 0.945 (0.032) | 0.927 (0.026) | |||
| SDSS | GBoost | 0.795 (0.017) | QSO | 0.725 (0.049) | 0.704 (0.046) | 0.713 (0.035) |
| GAL | 0.732 (0.062) | 0.737 (0.055) | 0.734 (0.053) | |||
| STA | 0.916 (0.032) | 0.943 (0.032) | 0.929 (0.021) | |||
| SDSS | SVC | 0.789 (0.026) | QSO | 0.738 (0.075) | 0.662 (0.060) | 0.696 (0.055) |
| GAL | 0.714 (0.070) | 0.747 (0.059) | 0.729 (0.058) | |||
| STA | 0.901 (0.039) | 0.959 (0.020) | 0.928 (0.017) | |||
| SDSS | FNN | 0.871 (0.015) | QSO | 0.730 (0.040) | 0.650 (0.050) | 0.680 (0.030) |
| GAL | 0.901 (0.017) | 0.915 (0.013) | 0.908 (0.012) | |||
| STA | 0.880 (0.017) | 0.916 (0.016) | 0.898 (0.015) | |||
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