. Author manuscript; available in PMC: 2021 Aug 5.

Published in final edited form as: Expert Syst Appl. 2021 Feb 23;174:114740. doi: 10.1016/j.eswa.2021.114740

Table 3.

Multi-label classification performance of four general-purpose classifiers and three convolutional neural networks. Performance measured using macro-averaged precision (Prec.^M), macro-averaged recall (Rec.^M), macro-averaged F1-score (F1^M) and F2-score (F2^M), macro-averaged Mathew’s Correlation Coefficient (MCC^M), Jaccard Index (JI), and exact match ratio (EMR). Best values for each measure highlighted in bold.

Classifier	Prec.^M	Rec.^M	F1^M	F2^M	MCC^M	JI	EMR
SVM	0.59	0.66	0.52	0.63	0.36	0.41	0.02
NB	0.48	0.69	0.53	0.64	0.31	0.43	0.05
KNN	0.76	0.52	0.55	0.67	0.44	0.60	0.31
RF	0.83	0.60	0.66	0.76	0.60	0.71	0.45
CNN: Cartesian	0.88	0.86	0.87	0.87	0.81	0.83	0.65
CNN: Polar-min	0.88	0.84	0.85	0.86	0.80	0.83	0.63
CNN: Polar-max	0.89	0.84	0.86	0.86	0.81	0.83	0.65