Table 4.
Performance measures of data mining algorithm at different levels of significance over Antibodies dataset
|
SIGNIFICANCE |
p < 5 x 10-8 |
p < 5 x 10-7 |
p < 5 x 10-6 |
p < 5 x 10-5 |
|
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Algorithm | Acc. | Sp | Sn | AUC | Acc. | Sp | Sn | AUC | Acc. | Sp | Sn | AUC | Acc | Sp | Sn | AUC | Avg. |
|
R. Forest |
90.0 |
93.0 |
90.0 |
0.96 |
90.0 |
91.0 |
90.0 |
0.97 |
92.0 |
94.0 |
92.0 |
0.96 |
94.0 |
96.0 |
94.0 |
0.97 |
93.3 |
|
Bayes Net |
88.0 |
92.0 |
88.0 |
0.96 |
88.0 |
91.0 |
88.0 |
0.96 |
94.0 |
95.0 |
94.0 |
0.95 |
92.0 |
95.0 |
92.0 |
0.96 |
92.5 |
|
Naïve Bayes |
88.0 |
94.0 |
88.0 |
0.96 |
88.0 |
94.0 |
88.0 |
0.96 |
88.0 |
94.0 |
88.0 |
0.96 |
88.0 |
94.0 |
88.0 |
0.96 |
91.5 |
|
SVM |
80.0 |
86.6 |
80.0 |
0.86 |
86.0 |
89.9 |
86.0 |
0.89 |
94.0 |
96.6 |
97.0 |
0.95 |
96.0 |
96.9 |
96.0 |
0.96 |
90.7 |
|
MLP |
80.0 |
89.8 |
80.0 |
0.91 |
86.0 |
89.9 |
86.0 |
0.96 |
94.0 |
96.6 |
94.0 |
0.99 |
dnf |
dnf |
dnf |
dnf |
90.2* |
|
SLR |
84.0 |
91.6 |
84.0 |
0.89 |
86.0 |
83.2 |
86.0 |
0.92 |
90.0 |
93.5 |
90.0 |
0.97 |
92.0 |
95.0 |
92.0 |
0.96 |
90.1 |
|
KNN |
82.0 |
90.7 |
82.0 |
0.92 |
84.0 |
88.7 |
84.0 |
0.94 |
86.0 |
91.2 |
86.0 |
0.95 |
92.0 |
96.4 |
92.0 |
0.95 |
89.4 |
|
Logistic R. |
72.0 |
85.3 |
72.0 |
0.92 |
84.0 |
90.1 |
84.0 |
0.93 |
92.0 |
96.4 |
92.0 |
0.98 |
90.0 |
96.1 |
90.0 |
0.98 |
89.1 |
|
M5P |
80.0 |
91.5 |
80.0 |
0.92 |
76.0 |
87.4 |
76.0 |
0.90 |
78.0 |
89.4 |
78.0 |
0.91 |
74.0 |
85.4 |
74.0 |
0.89 |
83.2 |
|
Hyper Pipes |
64.0 |
83.6 |
64.0 |
0.90 |
72.0 |
84.9 |
72.0 |
0.90 |
80.0 |
87.5 |
80.0 |
0.92 |
80.0 |
87.1 |
80.0 |
0.93 |
81.3 |
|
K star |
88.0 |
93.4 |
88.0 |
0.94 |
94.0 |
97.2 |
94.0 |
0.95 |
82.0 |
91.8 |
82.0 |
0.93 |
20.0 |
90.2 |
20.8 |
0.68 |
80.7 |
|
J48 |
80.0 |
92.5 |
80.0 |
0.86 |
72.0 |
87.0 |
72.0 |
0.87 |
70.0 |
87.6 |
70.0 |
0.79 |
64.0 |
86.1 |
64.0 |
0.77 |
78.4 |
|
ASC |
82.0 |
91.7 |
82.0 |
0.87 |
72.0 |
82.9 |
72.0 |
0.82 |
70.0 |
87.8 |
70.0 |
0.76 |
64.0 |
88.5 |
64.0 |
0.75 |
77.9 |
|
Random Tree |
72.0 |
90.3 |
72.0 |
0.81 |
64.0 |
82.1 |
64.0 |
0.73 |
68.0 |
87.7 |
68.0 |
0.78 |
74.0 |
89.7 |
74.0 |
0.82 |
76.2 |
|
VFI |
72.0 |
88.5 |
72.0 |
0.86 |
64.0 |
91.9 |
64.0 |
0.85 |
58.0 |
94.7 |
58.0 |
0.86 |
52.0 |
94.5 |
52.0 |
0.89 |
75.5 |
|
LDA |
68.0 |
84.5 |
68.0 |
0.88 |
40.0 |
81.1 |
40.0 |
0.71 |
42.0 |
89.7 |
48.8 |
0.54 |
20.0 |
88.4 |
25.0 |
0.58 |
60.4 |
| K means | 46.0 | 68.7 | 46.0 | 0.57 | 46.0 | 68.7 | 46.0 | 0.57 | 40.0 | 68.1 | 40.0 | 0.54 | 40.0 | 68.1 | 40.0 | 0.54 | 52.5 |
Acc: Accuracy, Sp: Specificity, Sn: Sensitivity, AUC: Area under ROC curve, Avg: Average score in % for each algorithms, dnf: Did not Finish”, * denotes Avg. from 3 significance levels. Measures >90% are marked in bold.