Table 1 Open in new tab Example-based...

Table 1

Example-based evaluation of the predictive performance of different multi-label classifiers

	Hamming loss	Accuracy	Precision	Recall	F1-measure	Subset accuracy
MLCDForest	0.1145	0.6978	0.8402	0.7400	0. 6978	0.3347
DBPNN	0.2118	0.4811	0.8258	0.4999	0.5997	0.1636
RAkEL [36]	0.2032	0.5471	0.7781	0.6133	0.6409	0.1980
MLkNN [36]	0.1970	0.5610	0.7599	0.6486	0.6627	0.1807
BR [36]	0.2048	0.5441	0.7804	0.6050	0.6405	0. 1965
BPMLL [36]	0.2241	0.5191	0.6900	0.6660	0.6412	0.1006

	Hamming loss	Accuracy	Precision	Recall	F1-measure	Subset accuracy
MLCDForest	0.1145	0.6978	0.8402	0.7400	0. 6978	0.3347
DBPNN	0.2118	0.4811	0.8258	0.4999	0.5997	0.1636
RAkEL [36]	0.2032	0.5471	0.7781	0.6133	0.6409	0.1980
MLkNN [36]	0.1970	0.5610	0.7599	0.6486	0.6627	0.1807
BR [36]	0.2048	0.5441	0.7804	0.6050	0.6405	0. 1965
BPMLL [36]	0.2241	0.5191	0.6900	0.6660	0.6412	0.1006