Multiteam Competitive Optimization Algorithm and Its Application in Bearing Fault Diagnosis

Table 4

The comparison of test samples recognition accuracy for Haberman’s survival.

Algorithm	Recognition accuracy 1	Recognition accuracy 2	Recognition accuracy 3
K-MTCO	69.52% (73/105)	69.52% (73/105)	69.52% (73/105)
	(60/65) (13/40)	(61/65) (12/40)	(60/65) (13/40)
S-MTCO	68.57% (72/105)	68.57% (72/105)	68.57% (72/105)
	(61/65) (11/40)	(59/65) (13/40)	(61/65) (11/40)
SVM	44.76% (47/105)	44.76% (47/105)	44.76% (47/105)
	(19/65) (28/40)	(19/65) (28/40)	(20/65) (27/40)
BP	67.62% (71/105)	60% (63/105)	58.10% (61/105)
	(62/65) (9/40)	(48/65) (15/40)	(50/65) (11/40)
LVQ	53.33% (56/105)	61.90% (65/105)	64.76% (68/105)
	(45/65) (11/40)	(55/65) (10/40)	(58/65) (10/40)
k-NN	k = 1 : 53.33% (56/105)	k = 10 : 67.62% (71/105)	k = 3 : 49.52% (52/105)
	(33/65) (23/40)	(58/65) (13/40)	(31/65) (21/40)