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Table 4 Precision, recall and F1-score for the positive and negative classes using \({\mathcal {F}}_1\)

From: Semi-supervised learning for detecting human trafficking

Learner Precision Recall F1-score
\(class_p\) \(class_n\) \(class_p\) \(class_n\) \(class_p\) \(class_n\)
\(S^3VM-R\) 0.91 0.92 0.91 0.93 0.91 0.92
Laplacian SVM 0.86 0.89 0.88 0.9 0.87 0.88
LabelSpreading (RBF) 0.76 0.78 0.77 0.73 0.8 0.81
LabelSpreading (KNN) 0.65 0.7 0.71 0.68 0.69 0.73
Co-training (SVM) 0.81 0.84 0.71 0.92 0.73 0.87
SVM 0.86 0.83 0.68 0.96 0.74 0.88
KNN 0.72 0.8 0.63 0.88 0.65 0.83
Gaussian NB 0.79 0.81 0.72 0.85 0.73 0.81
Logistic regression 0.81 0.85 0.71 0.93 0.74 0.88
AdaBoost 0.86 0.83 0.68 0.95 0.74 0.88
Random forest 0.77 0.85 0.73 0.89 0.73 0.86
  1. Experiments were run using tenfold cross-validation on the labeled data. The best performance is in italic