1 Introduction
2 Related work
3 Probabilistic neural network
3.1 Bayesian classifier
3.2 Network’s structure
4 Proposed algorithms
4.1 Smoothing parameter selection
4.2 PNN structure reduction by the use of k-means clustering
4.3 PNN structure reduction by means of support vector machines
4.3.1 The meaning of C constraint
4.3.2 The use of kernel function
4.3.3 The proposed approach
5 Input data used to test the models
-
Wisconsin breast cancer (WBC) set with 683 patterns and 9 features. The data are divided into two groups: 444 benign cases and 239 malignant cases.
-
Pima Indians diabetes (PID) set with 768 patterns and 8 features. Two classes of data are considered: samples tested negative (500 women) and samples tested positive (268 women).
-
Haberman’s survival (HS) set with 306 patterns and 3 features. There are two input classes: patients who survived 5 years or longer (225 records) and patients who died within 5 years (81 records).
-
Cardiotocography (CTG) set with 2126 patterns and 22 features. The classes are coded into three states: normal (1655 cases), suspect (295 cases) and pathological (176 cases).
-
Thyroid (T) set with 7200 patterns and 21 features. Three classes are regarded: subnormal functioning (166 samples), hyperfunction (368 samples) and not hypothyroid (6666 samples).
-
Dermatology (D) set with 358 patterns and 34 features. Six data classes are considered: psoriasis (111 cases), seborrheic dermatitis (60 cases), lichen planus (71 cases), chronic dermatitis (48 cases), pityriasis rosea (48 cases) and pityriasis rubra pilaris (20 cases).
-
Diagnostic Wisconsin breast cancer (DWBC) set with 569 patterns and 30 features. Two medical states are regarded: malignant (212 instances) and benign (357 instances).
6 Results and discussion
6.1 Results for the proposed approaches
Algorithm 1 | Algorithm 2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s
|
\(c_{s}\)
| Acc | Sen | Spe |
J
| Time (s) |
sc
| SVs | Acc | Sen | Spe |
Q
| Time (s) |
1 | 68 | 0.971 | 0.958 | 0.977 | 0.968 | 0.94 | 1.2 | 65 | 0.677 | 0.767 | 0.500 | 0.669 | 0.41 |
2 | 137 | 0.993 | 1.000 | 0.988 |
0.994
| 2.62 | 1.5 | 69 | 0.681 | 0.773 | 0.520 | 0.676 | 0.92 |
3 | 205 | 0.966 | 0.972 | 0.962 | 0.967 | 5.74 | 2 | 80 | 0.750 | 0.818 | 0.600 | 0.740 | 1.14 |
4 | 274 | 0.975 | 0.968 | 0.977 | 0.973 | 8.27 | 5 | 218 | 0.954 | 0.971 | 0.889 | 0.946 | 8.27 |
5 | 342 | 0.976 | 0.983 | 0.973 | 0.978 | 12.09 | 10 | 293 | 0.966 | 0.885 | 0.987 | 0.946 | 19.08 |
6 | 409 | 0.976 | 0.965 | 0.981 | 0.974 | 16.91 | 50 | 397 | 0.982 | 0.992 | 0.968 | 0.982 | 17.31 |
7 | 478 | 0.981 | 0.988 | 0.977 | 0.982 | 25.27 | 80 | 432 | 0.969 | 0.983 | 0.953 | 0.970 | 20.30 |
8 | 546 | 0.985 | 0.989 | 0.983 | 0.986 | 30.99 | 100 | 439 | 0.982 | 0.970 | 0.992 | 0.980 | 24.99 |
9 | 615 | 0.985 | 0.991 | 0.983 | 0.986 | 31.64 | 200 | 449 | 0.982 | 0.987 | 0.976 | 0.982 | 29.53 |
All | 683 | 0.987 | 0.987 | 0.986 | 0.987 | 46.71 | 500 | 449 | 0.984 | 0.987 | 0.981 |
0.984
| 35.31 |
Algorithm 1 | Algorithm 2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s
|
\(c_{s}\)
| Acc | Sen | Spe |
J
| Time (s) |
sc
| SVs | Acc | Sen | Spe |
Q
| Time (s) |
1 | 77 | 0.909 | 0.852 | 0.940 |
0.898
| 2.07 | 1.2 | 374 | 0.486 | 0.259 | 0.709 | 0.463 | 2.49 |
2 | 154 | 0.759 | 0.611 | 0.840 | 0.731 | 4.45 | 1.5 | 385 | 0.525 | 0.262 | 0.773 | 0.496 | 3.97 |
3 | 230 | 0.800 | 0.675 | 0.867 | 0.776 | 9.50 | 2 | 384 | 0.521 | 0.312 | 0.733 | 0.501 | 7.38 |
4 | 307 | 0.801 | 0.626 | 0.895 | 0.767 | 15.51 | 5 | 386 | 0.588 | 0.345 | 0.794 | 0.556 | 12.32 |
5 | 384 | 0.794 | 0.619 | 0.888 | 0.760 | 19.10 | 10 | 407 | 0.636 | 0.389 | 0.826 | 0.600 | 13.17 |
6 | 461 | 0.757 | 0.528 | 0.880 | 0.713 | 19.92 | 50 | 664 | 0.738 | 0.574 | 0.847 | 0.711 | 31.18 |
7 | 538 | 0.797 | 0.622 | 0.891 | 0.763 | 39.06 | 80 | 725 | 0.774 | 0.608 | 0.871 | 0.744 | 38.75 |
8 | 614 | 0.764 | 0.556 | 0.875 | 0.724 | 40.84 | 100 | 742 | 0.784 | 0.876 | 0.623 |
0.779
| 40.15 |
9 | 691 | 0.769 | 0.573 | 0.876 | 0.732 | 39.79 | 200 | 768 | 0.778 | 0.608 | 0.870 | 0.745 | 45.54 |
All | 768 | 0.778 | 0.608 | 0.870 | 0.745 | 44.02 | 500 | 768 | 0.778 | 0.608 | 0.870 | 0.745 | 45.51 |
Algorithm 1 | Algorithm 2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s
|
\(c_{s}\)
| Acc | Sen | Spe |
J
| Time (s) |
sc
| SVs | Acc | Sen | Spe |
Q
| Time (s) |
1 | 31 | 0.677 | 0.250 | 0.826 | 0.579 | 0.11 | 1.2 | 170 | 0.524 | 0.000 | 1.000 | 0.462 | 0.64 |
2 | 61 | 0.754 | 0.313 | 0.911 | 0.653 | 0.15 | 1.5 | 169 | 0.550 | 0.914 | 0.216 | 0.592 | 0.55 |
3 | 92 | 0.761 | 0.083 | 1.000 | 0.605 | 0.17 | 2 | 169 | 0.538 | 0.049 | 0.988 | 0.481 | 0.64 |
4 | 122 | 0.778 | 0.375 | 0.922 |
0.686
| 0.57 | 5 | 171 | 0.549 | 0.062 | 0.989 | 0.491 | 0.67 |
5 | 154 | 0.747 | 0.268 | 0.920 | 0.638 | 0.61 | 10 | 174 | 0.528 | 0.025 | 0.957 | 0.463 | 0.70 |
6 | 184 | 0.761 | 0.102 | 1.000 | 0.611 | 0.47 | 50 | 200 | 0.600 | 0.062 | 0.966 | 0.512 | 1.72 |
7 | 215 | 0.744 | 0.140 | 0.962 | 0.606 | 0.71 | 80 | 215 | 0.637 | 0.148 | 0.933 | 0.550 | 2.01 |
8 | 245 | 0.735 | 0.077 | 0.972 | 0.585 | 1.57 | 100 | 224 | 0.687 | 0.308 | 0.902 | 0.616 | 2.42 |
9 | 276 | 0.768 | 0.246 | 0.956 | 0.649 | 1.65 | 200 | 243 | 0.695 | 0.259 | 0.914 | 0.608 | 2.70 |
All | 306 | 0.761 | 0.247 | 0.946 | 0.644 | 2.12 | 500 | 266 | 0.741 | 0.333 | 0.919 |
0.654
| 2.79 |
Algorithm 1 | Algorithm 2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s
|
\(c_{s}\)
|
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
J
| Time (s) |
sc
| SVs |
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
Q
| Time (s) |
1 | 214 | 0.981 | 0.920 | 0.979 | 0.955 | 9.26 | 1.2 | 230 | 0.925 | 0.892 | 0.941 | 0.941 | 12.17 |
2 | 425 | 0.980 | 0.912 | 0.978 | 0.952 | 31.61 | 1.5 | 247 | 0.933 | 0.897 | 0.944 | 0.945 | 12.78 |
3 | 639 | 0.982 | 0.916 | 0.974 | 0.957 | 84.11 | 2 | 288 | 0.928 | 0.886 | 0.942 | 0.933 | 20.92 |
4 | 850 | 0.981 | 0.920 | 0.975 | 0.957 | 119.93 | 5 | 600 | 0.971 | 0.941 | 0.972 | 0.969 | 117.65 |
5 | 1064 | 0.984 | 0.936 | 0.977 | 0.965 | 342.56 | 10 | 1069 | 0.981 | 0.959 | 0.981 | 0.978 | 215.08 |
6 | 1276 | 0.990 | 0.968 | 0.986 | 0.986 | 348.41 | 50 | 1985 | 0.991 | 0.977 | 0.989 |
0.989
| 605.79 |
7 | 1489 | 0.981 | 0.948 | 0.980 | 0.973 | 330.78 | 80 | 2070 | 0.979 | 0.917 | 0.979 | 0.954 | 610.80 |
8 | 1701 | 0.985 | 0.939 | 0.974 | 0.968 | 583.02 | 100 | 2084 | 0.990 | 0.970 | 0.986 | 0.986 | 777.62 |
9 | 1914 | 0.991 | 0.976 | 0.989 |
0.988
| 814.52 | 200 | 2098 | 0.992 | 0.975 | 0.989 | 0.988 | 745.51 |
All | 2126 | 0.991 | 0.973 | 0.989 | 0.987 | 887.88 | 500 | 2110 | 0.992 | 0.978 | 0.991 | 0.989 | 757.76 |
Algorithm 1 | Algorithm 2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s
|
\(c_{s}\)
|
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
J
| Time (s) |
sc
| SVs |
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
Q
| Time (s) |
1 | 721 | 0.963 | 0.669 | 0.787 | 0.825 | 52.36 | 1.2 | 944 | 0.955 | 0.921 | 0.966 | 0.951 | 159.35 |
2 | 1440 | 0.985 | 0.880 | 0.934 | 0.944 | 112.96 | 1.5 | 943 | 0.947 | 0.913 | 0.959 | 0.944 | 180.56 |
3 | 2160 | 0.989 | 0.939 | 0.979 | 0.974 | 375.21 | 2 | 1009 | 0.949 | 0.900 | 0.957 | 0.940 | 202.41 |
4 | 2879 | 0.981 | 0.881 | 0.941 | 0.943 | 655.14 | 5 | 1187 | 0.954 | 0.913 | 0.964 | 0.948 | 289.59 |
5 | 3600 | 0.982 | 0.895 | 0.957 | 0.953 | 1380.51 | 10 | 1262 | 0.959 | 0.905 | 0.965 | 0.949 | 319.21 |
6 | 4321 | 0.982 | 0.891 | 0.950 | 0.950 | 1898.36 | 50 | 1963 | 0.977 | 0.936 | 0.974 | 0.964 | 635.75 |
7 | 5040 | 0.982 | 0.874 | 0.939 | 0.944 | 2098.01 | 80 | 2365 | 0.980 | 0.939 | 0.978 | 0.968 | 965.03 |
8 | 5760 | 0.983 | 0.903 | 0.951 | 0.953 | 2293.61 | 100 | 2598 | 0.981 | 0.929 | 0.973 | 0.964 | 1128.61 |
9 | 6479 | 0.981 | 0.894 | 0.944 | 0.947 | 4338.36 | 200 | 3449 | 0.987 | 0.951 | 0.977 | 0.974 | 1500.32 |
All | 7200 | 0.994 | 0.963 | 0.985 |
0.985
| 7543.13 | 500 | 5021 | 0.991 | 0.960 | 0.980 |
0.980
| 2833.95 |
Algorithm 1 | Algorithm 2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s
|
\(c_{s}\)
|
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
J
| Time (s) |
sc
| SVs |
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
Q
| Time (s) |
1 | 36 | 0.991 | 0.917 | 0.995 | 0.975 | 0.35 | 1.2 | 257 | 0.999 | 0.996 | 0.999 | 0.998 | 23.34 |
2 | 72 | 0.999 | 0.999 | 0.999 |
0.999
| 0.67 | 1.5 | 282 | 0.998 | 0.991 | 0.998 | 0.997 | 24.24 |
3 | 106 | 0.991 | 0.967 | 0.994 | 0.986 | 2.82 | 2 | 319 | 0.999 | 0.997 | 0.999 |
0.999
| 29.96 |
4 | 142 | 0.995 | 0.985 | 0.997 | 0.994 | 3.23 | 5 | 356 | 0.999 | 0.997 | 1.000 | 0.999 | 33.31 |
5 | 180 | 0.998 | 0.993 | 0.999 | 0.997 | 7.11 | 10 | 358 | 0.997 | 0.990 | 0.999 | 0.996 | 31.86 |
6 | 216 | 0.995 | 0.978 | 0.997 | 0.992 | 10.34 | 50 | 358 | 0.997 | 0.990 | 0.999 | 0.996 | 31.90 |
7 | 252 | 0.999 | 0.996 | 0.999 | 0.998 | 13.64 | 80 | 358 | 0.997 | 0.990 | 0.999 | 0.996 | 32.28 |
8 | 286 | 0.999 | 0.996 | 0.999 | 0.998 | 18.08 | 100 | 358 | 0.997 | 0.990 | 0.999 | 0.996 | 32.10 |
9 | 322 | 0.997 | 0.990 | 0.998 | 0.996 | 24.99 | 200 | 358 | 0.997 | 0.990 | 0.999 | 0.996 | 32.13 |
All | 358 | 0.997 | 0.990 | 0.998 | 0.996 | 34.88 | 500 | 358 | 0.997 | 0.990 | 0.999 | 0.996 | 31.79 |
Algorithm 1 | Algorithm 2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s
|
\(c_{s}\)
| Acc | Sen | Spe |
J
| Time (s) |
sc
| SVs | Acc | Sen | Spe |
Q
| Time (s) |
1 | 57 | 0.999 | 0.997 | 0.998 |
0.998
| 0.62 | 1.2 | 206 | 0.976 | 0.980 | 0.971 | 0.976 | 56.48 |
2 | 113 | 0.991 | 1.000 | 0.976 | 0.991 | 2.67 | 1.5 | 211 | 0.976 | 0.963 | 0.990 | 0.975 | 62.20 |
3 | 171 | 0.998 | 0.997 | 0.997 | 0.997 | 7.48 | 2 | 226 | 0.982 | 1.000 | 0.965 | 0.984 | 74.32 |
4 | 228 | 0.997 | 0.995 | 0.994 | 0.996 | 15.78 | 5 | 335 | 0.976 | 0.976 | 0.976 | 0.976 | 107.65 |
5 | 285 | 0.986 | 0.983 | 0.991 | 0.986 | 22.41 | 10 | 442 | 0.989 | 0.981 | 0.996 | 0.988 | 132.06 |
6 | 341 | 0.982 | 0.995 | 0.961 | 0.982 | 30.78 | 50 | 568 | 0.993 | 0.986 | 0.997 |
0.992
| 183.98 |
7 | 398 | 0.992 | 0.996 | 0.986 | 0.992 | 50.52 | 80 | 569 | 0.993 | 0.986 | 0.997 | 0.992 | 189.27 |
8 | 456 | 0.991 | 0.993 | 0.988 | 0.991 | 92.91 | 100 | 569 | 0.993 | 0.986 | 0.997 | 0.992 | 189.27 |
9 | 512 | 0.988 | 0.997 | 0.974 | 0.988 | 104.80 | 200 | 569 | 0.993 | 0.986 | 0.997 | 0.992 | 189.27 |
All | 569 | 0.993 | 0.997 | 0.986 | 0.993 | 181.80 | 500 | 569 | 0.993 | 0.986 | 0.997 | 0.992 | 189.27 |
Algorithm 1 | Algorithm 2 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s
|
\(c_{s}\)
| Acc | Sen | Spe |
J
| Time (s) |
sc
| SVs | Acc | Sen | Spe |
Q
| Time (s) |
1 | 20 | 0.650 | 0.143 | 0.923 | 0.553 | 0.16 | 1.2 | 155 | 0.826 | 0.867 | 0.793 | 0.832 | 5.16 |
2 | 40 | 0.990 | 1.000 | 0.985 |
0.992
| 0.47 | 1.5 | 163 | 0.883 | 0.882 | 0.884 | 0.883 | 6.52 |
3 | 59 | 0.966 | 0.950 | 0.974 | 0.963 | 1.61 | 2 | 171 | 0.859 | 0.882 | 0.845 | 0.863 | 6.07 |
4 | 79 | 0.975 | 0.963 | 0.981 | 0.973 | 2.28 | 5 | 183 | 0.853 | 0.794 | 0.887 | 0.842 | 5.92 |
5 | 100 | 0.990 | 1.000 | 0.985 | 0.992 | 3.23 | 10 | 189 | 0.867 | 0.853 | 0.876 | 0.865 | 6.09 |
6 | 120 | 0.933 | 0.902 | 0.949 | 0.927 | 3.48 | 50 | 195 | 0.892 | 0.867 | 0.905 |
0.887
| 9.86 |
7 | 140 | 0.914 | 0.854 | 0.946 | 0.902 | 4.76 | 80 | 196 | 0.857 | 0.838 | 0.867 | 0.853 | 10.88 |
8 | 159 | 0.906 | 0.907 | 0.905 | 0.906 | 9.42 | 100 | 196 | 0.857 | 0.838 | 0.867 | 0.853 | 11.18 |
9 | 179 | 0.916 | 0.902 | 0.924 | 0.913 | 9.68 | 200 | 196 | 0.857 | 0.838 | 0.867 | 0.853 | 11.18 |
All | 199 | 0.864 | 0.867 | 0.863 | 0.865 | 11.89 | 500 | 197 | 0.878 | 0.824 | 0.907 | 0.868 | 9.91 |
Classifier | WBC | PID | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Acc | Sen | Spe |
J
| Time (s) | Acc | Sen | Spe |
J
| Time (s) | |
MLP | 0.968 | 0.949 | 0.977 | 0.964 | 3.94 | 0.769 | 0.578 | 0.872 |
0.732
| 3.81 |
SDT | 0.950 | 0.928 | 0.962 | 0.946 | 0.22 | 0.748 | 0.608 | 0.824 | 0.721 | 0.39 |
SVM | 0.972 | 0.979 | 0.968 |
0.973
| 11.33 | 0.772 | 0.548 | 0.892 | 0.729 | 291.98 |
k-means | 0.956 | 0.925 | 0.973 | 0.950 | 335.16 | 0.691 | 0.425 | 0.834 | 0.640 | 1.74 |
Classifier | HS | CTG | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Acc | Sen | Spe |
J
| Time (s) |
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
J
| Time (s) | |
MLP | 0.728 | 0.161 | 0.933 | 0.599 | 2.78 | 0.985 | 0.949 | 0.980 | 0.974 | 77.31 |
SDT | 0.748 | 0.395 | 0.875 |
0.668
| 0.21 | 0.991 | 0.977 | 0.986 |
0.990
| 0.38 |
SVM | 0.742 | 0.111 | 0.968 | 0.598 | 233.36 | 0.987 | 0.951 | 0.982 | 0.976 | 157.32 |
k-Means | 0.686 | 0.383 | 0.796 | 0.617 | 8.96 | 0.936 | 0.842 | 0.926 | 0.919 | 763.34 |
Classifier | T | D | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
J
| Time (s) |
\(\overline{\mathrm{Acc}}\)
|
\(\overline{\mathrm{Sen}}\)
|
\(\overline{\mathrm{Spe}}\)
|
J
| Time (s) | |
MLP | 0.966 | 0.645 | 0.806 | 0.825 | 146.03 | 0.988 | 0.963 | 0.993 | 0.984 | 5.16 |
SDT | 0.990 | 0.949 | 0.977 |
0.977
| 0.50 | 0.980 | 0.914 | 0.988 | 0.967 | 0.38 |
SVM | 0.986 | 0.868 | 0.936 | 0.945 | 451.57 | 0.991 | 0.969 | 0.994 |
0.987
| 12.50 |
k-Means | 0.895 | 0.634 | 0.797 | 0.794 | 122,040.77 | 0.966 | 0.885 | 0.980 | 0.951 | 410.75 |
6.2 Comparison to reference classifiers
Classifier | DWBC | OC | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Acc | Sen | Spe |
J
| Time (s) | Acc | Sen | Spe |
J
| Time (s) | |
MLP | 0.975 | 0.957 | 0.986 |
0.972
| 6.81 | 0.814 | 0.808 | 0.817 | 0.813 | 2.41 |
SDT | 0.936 | 0.896 | 0.961 | 0.929 | 0.54 | 0.758 | 0.750 | 0.763 | 0.757 | 0.25 |
SVM | 0.975 | 0.958 | 0.986 |
0.972
| 6.11 | 0.849 | 0.808 | 0.870 |
0.841
| 4.97 |
k-means | 0.891 | 0.778 | 0.958 | 0.871 | 98.17 | 0.758 | 0.779 | 0.748 | 0.762 | 0.59 |
6.3 Comparison to state-of-the-art procedures
Data set | Proposed approaches | Full PNN | Reference classifiers | State-of-the-art methods | |||||
---|---|---|---|---|---|---|---|---|---|
Algorithm 1 | Algorithm 2 | MLP | SDT | SVM |
k-Means | Source | Result | ||
WBC |
0.993
| 0.984 | 0.987 | 0.968 | 0.950 | 0.972 | 0.956 |
Georgiou et al. (2008) | 0.989 |
Azar and El-Said (2013) | 0.976 | ||||||||
PID |
0.909
| 0.784 | 0.778 | 0.769 | 0.748 | 0.772 | 0.691 |
Temurtas et al. (2009) | 0.781 |
Georgiou et al. (2006) | 0.753 | ||||||||
HS |
0.778
| 0.741 | 0.761 | 0.728 | 0.748 | 0.742 | 0.686 |
Chandra and Babu (2011) | 0.743 |
T | 0.989 | 0.991 |
0.994
| 0.966 | 0.990 | 0.986 | 0.895 |
Yeh and Lin (2011) | 0.983 |
Saiti et al. (2009) | 0.968 | ||||||||
D |
0.999
|
0.999
| 0.997 | 0.988 | 0.980 | 0.991 | 0.966 |
Chang et al. (2008) | 0.935 |
DWBC |
0.999
| 0.993 | 0.993 | 0.975 | 0.936 | 0.975 | 0.891 |
Chang et al. (2008) | 0.954 |