Multiteam Competitive Optimization Algorithm and Its Application in Bearing Fault Diagnosis

Table 5

The comparison of test samples recognition accuracy for seeds.

Algorithm	Recognition accuracy 1	Recognition accuracy 2	Recognition accuracy 3
K-MTCO	97.78% (88/90)	97.78% (88/90)	97.78% (88/90)
	(29/30) (30/30) (29/30)	(29/30) (30/30) (29/30)	(28/30) (30/30) (30/30)
S-MTCO	95.56% (86/90)	95.56% (86/90)	95.56% (86/90)
	(27/30) (30/30) (29/30)	(27/30) (30/30) (29/30)	(27/30) (30/30) (29/30)
SVM	95.56% (86/90)	95.56% (86/90)	95.56% (86/90)
	(27/30) (30/30) (29/30)	(27/30) (30/30) (29/30)	(27/30) (30/30) (29/30)
BP	94.44% (85/90)	95.56% (86/90)	96.67% (87/90)
	(25/30) (30/30) (30/30)	(26/30) (30/30) (30/30)	(27/30) (30/30) (30/30)
LVQ	95.56% (86/90)	94.44% (85/90)	94.44% (85/90)
	(27/30) (29/30) (30/30)	(27/30) (29/30) (29/30)	(27/30) (29/30) (29/30)
k-NN	k = 1 : 95.56% (86/90)	k = 4 : 97.78% (88/90)	k = 87 : 92.22% (83/90)
	(26/30) (30/30) (30/30)	(28/30) (30/30) (30/30)	(24/30) (30/30) (29/30)