Table 2 |
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|
Article filtering performance with different features and classifiers |
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|
Model |
Precision |
Recall |
F1 score |
AUC |
|
|
||||
|
Mean |
0.6642 |
0.7636 |
0.6868 |
0.7351 |
|
Standard deviation |
0.0810 |
0.1926 |
0.1035 |
0.0741 |
|
Best reported in terms of AUC [8] |
0.7080 |
0.8609 |
0.7770 |
0.8554 |
|
Our results in BioCreative 2006 |
0.7507 |
0.8107 |
0.7795 |
0.8471 |
|
|
||||
|
Term (baseline) |
0.7016 |
0.8213 |
0.7568 |
0.8037 |
|
String |
0.7044 |
0.8960 |
0.7887 |
0.8416 |
|
Named entity (NE) |
0.5815 |
0.9600 |
0.7243 |
0.7570 |
|
Template |
0.7841 |
0.7653 |
0.7746 |
0.8239 |
|
String + NE |
0.7360 |
0.8773 |
0.8005 |
0.8479 |
|
String + template |
0.7416 |
0.8880 |
0.8082 |
0.8372 |
|
String + NE + template |
0.7585 |
0.8373 |
0.7959 |
0.8507 |
|
String + term + NE + template |
0.7432 |
0.8720 |
0.8025 |
0.8608 |
|
|
||||
|
Naïve Bayes classifier |
0.6321 |
0.8613 |
0.7291 |
0.7884 |
|
Multinomial classifier |
0.6264 |
0.8720 |
0.7290 |
0.7770 |
|
Linear kernel SVM |
0.7016 |
0.8213 |
0.7568 |
0.8037 |
|
p-spectrum kernel SVM (p = 7) |
0.7352 |
0.8293 |
0.7794 |
0.8376 |
|
|
||||
|
Integration of the above four classifiers (AdaBoost) |
0.7995 |
0.8933 |
0.8438 |
0.8746 |
|
|
||||
|
This table shows the experimental results from article filtering. AUC, area under the receiving operator characteristic curve; SVM, support vector machine. |
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|
Huang et al. Genome Biology 2008 9(Suppl 2):S12 doi:10.1186/gb-2008-9-s2-s12 |
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