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Table 10 Comparison of each algorithm at the 100th iteration with 10-fold CV. on heart disease dataset

From: On convergence and complexity analysis of an accelerated forward–backward algorithm with linesearch technique for convex minimization problems and applications to data prediction and classification

 

Algorithm 1

Algorithm 2

Algorithm 3

Acc. train

Acc. test

Acc. train

Acc. test

Acc. train

Acc. test

Fold 1

80.95

73.33

81.68

76.67

84.98

90

Fold 2

80.15

87.10

80.15

87.10

83.09

93.55

Fold 3

79.04

61.29

79.78

64.52

86.76

67.74

Fold 4

81.99

74.19

81.99

74.19

84.93

77.42

Fold 5

79.49

76.67

79.85

76.67

84.62

83.33

Fold 6

81.68

76.67

81.68

76.67

86.45

83.33

Fold 7

82.78

83.33

82.78

83.33

86.08

80.00

Fold 8

80.95

76.67

80.95

76.67

86.81

86.67

Fold 9

75.82

93.33

75.82

93.33

83.88

96.67

Fold 10

80.22

80.00

79.85

80.00

84.62

80.00

Average Acc

80.31

78.26

80.45

78.91

85.22

83.87

\(\operatorname{ERR} _{ \% }\)

20.74

20.33

15.47

Training time (sec.)

0.0231

0.0351

0.0316