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Table 4 Performance evaluation of three proposed methods

From: Learning to predict expression efficacy of vectors in recombinant protein production

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

  1. 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.
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BMC Bioinformatics

ISSN: 1471-2105

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