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Table 5 Performance evaluations of various classification methods

From: Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

Methods

Global performance

Accuracy

Recall

Precision

CT(s)

EL-SCOA

98.35

98.35

98.37

12.00

EL

98.88

98.88

98.88

23.74

ANN

93.70

93.70

93.73

3.53

KNN

88.30

88.31

88.31

0.91

CFNN

97.17

97.17

97.16

8.36

FFNN

97.17

97.17

97.8

8.14

GRNN

97.06

97.06

97.06

7.06

SVM

92.14

92.14

92.16

15.69