Research Article
JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method
Table 2
Performance of ensemble learning method and individual base classifiers by 10-fold cross validation.
| Subfamily | Measure | Ensemble | RF | NB | LR | RBF network |
| Type I J-protein | Sn | 0.905 | 0.698 | 0.857 | 0.556 | 0 | Sp | 0.849 | 0.935 | 0.882 | 0.853 | 0.934 |
| Type II J-protein | Sn | 0.745 | 0.418 | 0.691 | 0.400 | 0 | Sp | 0.857 | 0.947 | 0.890 | 0.858 | 0.927 |
| Type III J-protein | Sn | 0.851 | 0.979 | 0.895 | 0.889 | 1 | Sp | 0.855 | 0.486 | 0.768 | 0.442 | 0 |
| Type IV J-protein | Sn | 1 | 0 | 0.700 | 0.200 | 0 | Sp | 0.849 | 0.938 | 0.884 | 0.848 | 0.900 |
| AvgSn | 0.875 | 0.524 | 0.786 | 0.511 | 0.250 |
| Acc | 0.852 | 0.922 | 0.881 | 0.837 | 0.885 |
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