Table 4.
Performance evaluation of three proposed methods
| Avg.\Methods | flatSVM | nestSVM | hierSVM | AdaBoost | |
|---|---|---|---|---|---|
| F1 measure | 0.7791 ± 0.0606 | 0.6989 ± 0.0578 | 0.7466 ± 0.0464 | 0.7241 ± 0.0287 | |
| F score | 0.7551 ± 0.0719 | 0.7068 ± 0.0600 | 0.7000 ± 0.0498 | 0.7075 ± 0.0442 | |
| Recall | 0.7833 ± 0.0998 | 0.7875 ± 0.0747 | 0.7083 ± 0.0900 | 0.7000 ± 0.0852 | |
| Precision | 0.7397 ± 0.1027 | 0.6466 ± 0.0795 | 0.7015 ± 0.0718 | 0.7240 ± 0.0567 | |
| Accuracy | 0.8351 ± 0.0488 | 0.7865 ± 0.0528 | 0.8041 ± 0.0320 | 0.8135 ± 0.0253 |
For F1 measure, each individual classifier must correctly identify each instance into its real class. In contrast, F score just focuses on measuring the rate of correctly identifying soluble fraction instances from non-soluble ones, i.e. the class of soluble fraction versus the other two classes.