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.

Chan et al. BMC Bioinformatics 2010 11(Suppl 1):S21   doi:10.1186/1471-2105-11-S1-S21

Open Data