Feature selection | CV | SVM-RFE | RF_based_FS |
---|
Classifier | FC | Equal-W | Equal-F | k-means | FC | Equal-W | Equal-F | k-means | FC | Equal-W | Equal-F | k-means |
---|
SVM
| 84.7 | 85.9 | 85.9 | 90.6 | 77.6 | 81.2 | 92.9 | 87.1 |
96.5
| 94.1 | 95.3 |
96.5
|
RF
| 85.9 | 85.9 | 84.7 | 85.9 | 81.2 | 78.8 | 88.2 | 87.1 | 91.7 |
92.9
| 91.7 | 90.6 |
NB
| 82.3 | 81.2 | 80.0 | 80.0 | 75.3 | 69.4 | 81.2 | 78.8 | 90.6 |
92.9
| 85.9 | 84.7 |
PAM
| 85.9 | 87.1 | 85.9 | 84.7 | 71.7 | 70.6 | 84.7 | 80.0 |
91.7
|
91.7
| 87.1 | 85.9 |
- The classification models were trained (257 samples) and tested (85 samples) on exon-array data. Highest accuracy for each classification method is marked in bold. While SVM in combination with RF_based_FS performed best whit the highest accuracy for both FC data (without discretization) and k-means discretised data, the other three classifiers (RF, NB and PAM) in combination with RF_based_FS achieved comparable classification accuracies on Eaual-W discretized data.