|
Methods
|
ACC (%)
|
SPC (%)
|
SEN (%)
|
F1_M (%)
|
G_M (%)
|
AUC (%)
|
|---|
|
ResCNN
|
90.22 (0.88)a
|
95.80 (1.23)
|
76.05 (3.21)
|
81.41 (1.74)
|
85.34 (1.59)
|
96.26 (0.73)
|
|
ResCNN + SMOTE
|
90.98 (1.07)
|
94.72 (1.34)
|
80.95 (3.50)
|
82.97 (2.05)
|
87.54 (1.75)
|
96.24 (0.84)
|
|
ResCNN + BSMOTE
|
90.76 (1.40)
|
95.48 (1.54)
|
78.10 (2.94)
|
82.12 (2.55)
|
86.34 (1.79)
|
96.27 (0.87)
|
|
ResCNN + UNDER
|
90.02 (1.68)
|
90.91 (1.71)
|
87.62 (3.71)
|
82.67 (2.82)
|
89.23 (2.14)
|
96.27 (0.80)
|
|
CS-ResCNN
|
92.24 (1.30)
|
93.19 (1.73)
|
89.66 (2.86)
|
86.00 (2.27)
|
91.39 (1.49)
|
97.11 (0.59)
|
|
CS-ResCNN + SMOTE
|
92.35 (1.01)
|
95.08 (0.93)
|
85.03 (4.91)
|
85.74 (2.21)
|
89.88 (2.31)
|
97.36 (0.70)
|
|
CS-ResCNN + BSMOTE
|
92.01 (0.86)
|
95.48 (1.04)
|
82.72 (3.78)
|
84.89 (1.83)
|
88.85 (1.80)
|
97.22 (0.65)
|
|
CS-ResCNN + UNDER
|
91.83 (0.85)
|
92.79 (1.74)
|
89.25 (3.80)
|
85.58 (1.42)
|
90.97 (1.41)
|
97.35 (0.61)
|
- ResCNN, residual convolutional neural network; CS-ResCNN, cost-sensitive residual convolutional neural network; SMOTE, synthetic minority over-sampling technique; BSMOTE, borderline-SMOTE; UNDER, under-sampling; CS-ResCNN + SMOTE, the combination of CS-ResCNN and SMOTE methods; CS-ResCNN + BSMOTE, the combination of CS-ResCNN and BSMOTE methods; CS-ResCNN + BSMOTE, the combination of CS-ResCNN and UNDER methods
- Italic represent the best value in all methods
-
aMean (standard deviation)
