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Table 5 Real-life datasets metrics. A 5-fold nested-cross validation approach is applied into real-life datasets. Mean (standard deviation) of 10 resampling datasets is shown. The table summarizes the mean (and standard deviation) of the following metrics: accuracy, area under the Receiver Operating Characteristic curve (AUC-ROC), sensitivity (Sn), specificity (Sp) and F1-score (F1)

From: Enhancing SVM for survival data using local invariances and weighting

  Lung Stanford2 PBC
Method Accuracy AUC-ROC Sn Sp F1 Accuracy AUC-ROC Sn Sp F1 Accuracy AUC-ROC Sn Sp F1
Cox model 0.61 (0.08) 0.60 (0.08) 0.52 (0.07) 0.39 (0.07) 0.50 (0.08) 0.61 (0.07) 0.61 (0.11) 0.48 (0.07) 0.42 (0.09) 0.51 (0.09) 0.75 (0.11) 0.85 (0.11) 0.58 (0.07) 0.62 (0.09) 0.68 (0.09)
Kernel Cox 0.66 (0.14) 0.52 (0.03) 0.60 (0.10) 0.25 (0.07) 0.49 (0.13) 0.51 (0.21) 0.50 (0.01) 0.56 (0.12) 0.31 (0.07) 0.45 (0.08) 0.55 (0.11) 0.50 (0.08) 0.52 (0.12) 0.30 (0.07) 0.55 (0.08)
wSVM-KM 0.73 (0.08) 0.68 (0.16) 0.63 (0.10) 0.34 (0.07) 0.59 (0.10) 0.59 (0.12) 0.62 (0.04) 0.57 (0.11) 0.29 (0.07) 0.52 (0.10) 0.70 (0.09) 0.75 (0.10) 0.67 (0.04) 0.65 (0.11) 0.39 (0.07)
wSVM-Prop 0.70 (0.12) 0.64 (0.15) 0.62 (0.12) 0.33 (0.07) 0.59 (0.12) 0.55 (0.09) 0.59 (0.10) 0.56 (0.12) 0.27 (0.07) 0.51 (0.12) 0.70 (0.07) 0.73 (0.05) 0.59 (0.10) 0.60 (0.12) 0.41 (0.09)
pSVM-linear-KM 0.89 (0.08) 0.72 (0.16) 0.63 (0.10) 0.23 (0.11) 0.62 (0.11) 0.78 (0.07) 0.63 (0.11) 0.57 (0.10) 0.21 (0.11) 0.58 (0.11) 0.67 (0.08) 0.84 (0.08) 0.65 (0.09) 0.61 (0.09) 0.35 (0.12)
pSVM-linear-prop 0.88 (0.08) 0.70 (0.15) 0.65 (0.16) 0.36 (0.13) 0.61 (0.11) 0.77 (0.07) 0.63 (0.10) 0.56 (0.16) 0.24 (0.13) 0.57 (0.11) 0.65 (0.12) 0.80 (0.10) 0.68 (0.13) 0.45 (0.11) 0.26 (0.13)
pSVM-radial-KM 0.82 (0.08) 0.70 (0.14) 0.69 (0.09) 0.55 (0.09) 0.63 (0.09) 0.60 (0.10) 0.61 (0.08) 0.43 (0.09) 0.45 (0.12) 0.50 (0.09) 0.70 (0.07) 0.65 (0.13) 0.65 (0.08) 0.50 (0.09) 0.55 (0.12)
pSVM-radial-prop 0.81 (0.08) 0.69 (0.15) 0.61 (0.11) 0.55 (0.07) 0.62 (0.10) 0.60 (0.11) 0.59 (0.10) 0.44 (0.06) 0.51 (0.11) 0.52 (0.10) 0.70 (0.07) 0.61 (0.10) 0.63 (0.10) 0.44 (0.06) 0.51 (0.07)
LUPI-linear-KM 0.92 (0.05) 0.65 (0.14) 0.72 (0.14) 0.67 (0.10) 0.69 (0.11) 0.80 (0.07) 0.63 (0.12) 0.65 (0.11) 0.51 (0.10) 0.61 (0.11) 0.70 (0.07) 0.63 (0.09) 0.63 (0.12) 0.55 (0.11) 0.50 (0.10)
LUPI-linear-prop 0.92 (0.05) 0.65 (0.14) 0.71 (0.11) 0.61 (0.12) 0.67 (0.09) 0.80 (0.07) 0.63 (0.12) 0.65 (0.10) 0.53 (0.13) 0.58 (0.09) 0.70 (0.07) 0.63 (0.09) 0.61 (0.12) 0.57 (0.13) 0.53 (0.13)
inSVM-gradient 0.67 (0.08) 0.68 (0.08) 0.60 (0.10) 0.43 (0.12) 0.58 (0.08) 0.52 (0.11) 0.59 (0.10) 0.49 (0.10) 0.43 (0.12) 0.58 (0.08) 0.68 (0.10) 0.60 (0.15) 0.59 (0.11) 0.52 (0.12) 0.46 (0.12)
inSVM-averaging 0.85 (0.07) 0.71 (0.13) 0.76 (0.13) 0.43 (0.07) 0.65 (0.12) 0.78 (0.06) 0.67 (0.13) 0.69 (0.13) 0.56 (0.12) 0.56 (0.09) 0.75 (0.02) 0.74 (0.10) 0.68 (0.11) 0.61 (0.12) 0.57 (0.13)