Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm

Table 2

Comparison among swarm intelligence-based sensor ontology matching techniques in terms of standard deviation.

Testing Case	CCEA	CETSA	CCFA	CDEA	SAPSO	CFPA
	1XX	0.01 (0.01, 0.01)	0.03 (0.02, 0.02)	0.01 (0.03, 0.02)	0.01 (0.01, 0.01)	0.01 (0.01, 0.01)	0.01 (0.01, 0.01)
2XX	0.01 (0.02, 0.01)	0.04 (0.02, 0.04)	0.02 (0.01, 0.03)	0.02 (0.02, 0.02)	0.03 (0.02, 0.03)	0.01 (0.01, 0.01)
3XX	0.02 (0.01, 0.03)	0.05 (0.02, 0.04)	0.02 (0.01, 0.03)	0.01 (0.02, 0.01)	0.02 (0.02, 0.01)	0.02 (0.01, 0.01)
MMI-SSN	0.01 (0.02, 0.02)	0.03 (0.03, 0.02)	0.03 (0.03, 0.02)	0.02 (0.03, 0.01)	0.04 (0.03, 0.02)	0.01 (0.01, 0.01)
CSIRO-SSN	0.03 (0.01, 0.01)	0.03 (0.02, 0.01)	0.03 (0.02, 0.02)	0.05 (0.03, 0.02)	0.03 (0.02, 0.02)	0.02 (0.01, 0.02)
MMI-CSIRO	0.03 (0.02, 0.01)	0.03 (0.01, 0.01)	0.02 (0.02, 0.03)	0.01 (0.01, 0.01)	0.04 (0.02, 0.04)	0.01 (0.01, 0.01)

The symbols , , and , respectively, stand for the standard deviation of f-measure, recall, and precision.