Semi-supervised learning for detecting human trafficking

Security Informatics

Table 6 Precision, recall and F1-score for the positive and negative classes using \(\{{\mathcal {F}}_1, {\mathcal {F}}_2\}\)

Learner	Precision		Recall		F1-score
Learner	\(class_p\)	\(class_n\)	\(class_p\)	\(class_n\)	\(class_p\)	\(class_n\)
\(S^3VM-R\)	0.97	0.97	0.95	0.98	0.94	0.95
Laplacian SVM	0.96	0.94	0.91	0.96	0.91	0.93
LabelSpreading (RBF)	0.83	0.86	0.82	0.84	0.81	0.86
LabelSpreading (KNN)	0.71	0.74	0.75	0.78	0.8	0.78
Co-training (SVM)	0.92	0.9	0.9	0.94	0.91	0.92
SVM	0.96	0.92	0.84	0.97	0.89	0.94
KNN	0.84	0.83	0.67	0.95	0.73	0.88
Gaussian NB	0.77	0.96	0.94	0.87	0.84	0.91
Logistic regression	0.95	0.9	0.79	0.97	0.85	0.93
AdaBoost	0.88	0.88	0.75	0.95	0.78	0.91
Random forest	0.93	0.9	0.82	0.97	0.86	0.93

Experiments were run using tenfold cross-validation on the labeled data. The best performance is in italic