PRE_ts |
PRE_tr |
SEN_ts |
SEN_tr |
ACC_ts |
ACC_tr |
|
93.085 |
95.51 |
86.179 |
90.231 |
81.14 |
87.464 |
Iter1 |
93.548 |
95.853 |
87.097 |
89.873 |
82.456 |
87.755 |
Iter2 |
93.784 |
95.51 |
81.343 |
90.933 |
78.947 |
87.901 |
Iter3 |
93.548 |
95.005 |
89.256 |
89.147 |
83.772 |
86.006 |
Iter4 |
93.316 |
96.201 |
85.6 |
89.75 |
81.14 |
88.192 |
Iter5 |
92.188 |
95.34 |
88.696 |
90.181 |
81.14 |
87.172 |
Iter6 |
93.784 |
94.839 |
92.373 |
90.026 |
86.404 |
86.297 |
Iter7 |
95 |
95.767 |
89.063 |
89.169 |
85.965 |
87.172 |
Iter8 |
94.262 |
94.348 |
88.095 |
87.76 |
84.211 |
84.111 |
Iter9 |
96.023 |
94.107 |
85.507 |
90.027 |
85.088 |
85.131 |
Iter10 |
93.854 |
95.248 |
87.321 |
89.71 |
83.026 |
86.72 |
mean |
1.1154 |
0.44753 |
8.6809 |
0.7364 |
5.9869 |
1.7352 |
var |
Table 1: Linear kernel classification results
PRE_ts |
PRE_tr |
SEN_ts |
SEN_tr |
ACC_ts |
ACC_tr |
|
94.022 |
99.694 |
96.491 |
100 |
88.596 |
99.563 |
Iter1 |
96.286 |
99.897 |
99.167 |
100 |
93.86 |
99.854 |
Iter2 |
96.552 |
100 |
96 |
100 |
92.544 |
100 |
Iter3 |
96.023 |
100 |
94.4 |
100 |
91.667 |
100 |
Iter4 |
95.506 |
99.897 |
96.667 |
99.749 |
91.667 |
99.708 |
Iter5 |
96.286 |
99.795 |
99.145 |
100 |
92.982 |
99.708 |
Iter6 |
96.286 |
99.897 |
97.541 |
100 |
93.421 |
99.854 |
Iter7 |
95 |
100 |
99.13 |
100 |
91.667 |
100 |
Iter8 |
93.316 |
99.897 |
96.396 |
100 |
88.158 |
99.854 |
Iter9 |
95.506 |
99.897 |
98.305 |
100 |
92.105 |
99.854 |
Iter10 |
95.4 |
99.898 |
97.324 |
99.975 |
91.667 |
99.84 |
mean |
1.0802 |
0.0092839 |
2.586 |
0.0062814 |
3.5908 |
0.021014 |
var |
Table 2: RBF kernel classification results
PRE_ts |
PRE_tr |
SEN_ts |
SEN_tr |
ACC_ts |
ACC_tr |
|
94.505 |
99.897 |
96.552 |
100 |
90.351 |
99.854 |
Iter1 |
93.784 |
99.795 |
98.198 |
100 |
89.474 |
99.708 |
Iter2 |
96.023 |
99.795 |
98 ٬ 333 |
100 |
93.421 |
99.708 |
Iter3 |
94.262 |
99.795 |
98.23 |
100 |
89.912 |
99.708 |
Iter4 |
95.506 |
99.897 |
97.479 |
100 |
91.667 |
99.854 |
Iter5 |
94.262 |
99.795 |
97.368 |
100 |
89.912 |
100 |
Iter6 |
95.251 |
100 |
98.291 |
100 |
91.667 |
99.854 |
Iter7 |
92.188 |
99.897 |
98.077 |
100 |
86.404 |
99.854 |
Iter8 |
95.763 |
99 ٬ 795 |
97.5 |
100 |
92.544 |
99.708 |
Iter9 |
95 |
99.795 |
96.61 |
100 |
90.351 |
99.708 |
Iter10 |
94.654 |
99 ٬ 857 |
97.664 |
100 |
90.57 |
99.796 |
mean |
1.2749 |
0.0051142 |
0.45702 |
0 |
3.7725 |
0.010389 |
var |
Table 3: Classification results with polynomial kernels
Figure 2: Fusion matrix with linear core
Figure 3: Decomposition matrix with RBF core
Figure 4: Decomposition matrix with polynomial nucleus
Tables at a glance
Figures at a glance