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For the first testing set, the best average accuracy for ANCESCON is about 0.5, while the best average reconstruction accuracies for PAML, PHYLIP and PAUP* are 0.45, 0.39 and 0.32 respectively.
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Our evaluation using a manually annotated data set showed that the two-stage classifier outperformed the single-stage classifier and achieved the best average accuracy of 83.7% for the eight most frequently mentioned genes when optimized feature sets were used.
Because it has presented best average accuracy performance for experiments with more than 50%% labeled patterns and average performance for the rest.
Of the tested ML-based methods, the best average accuracy is achieved for kNN, with k=5.
However, for most of these targets, the difference between the best average accuracy and that obtained with Bayesian optimization was approximately 3% (however, for example for beta3AR this difference approached to 10%, from 0.879 to 0.972).
As already said, the resulting method VNS+SVM gives the best average accuracy value which is equal to 77.46% for the German dataset.
For German dataset, VNS+SVM gives the best average accuracy value equals to 77.46% compared to the all considered classifiers.
CBD provides the second best average accuracy on the problems tested as well as the best reliability (the lowest standard deviation).
For the two-class classification that distinguishes between Set E and Sets (A, B, C, D) [26, 27], the approach presented here achieved the best average accuracies, which were 1%-21%-2%her then the methods described in the literature.
The average accuracy for all four receptors is (sim) 94%.
The average accuracy for this technique reached 94.8%.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com