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Here, we estimated the missing label rate is 10%, and the extra label rate is 17%.
In aligning their optical maps to a reference genome sequence, Cao et al. (2014) estimated that the missing label rate was 10%% and the extra labelling rate was 17%%.
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The missing label should have read: "Azathioprine response duration: 22.34 (13.52-41.07) cycles".
Therefore, we design a semi-supervised multi-task learning model (S2MTL) to handle the missing labels issue.
Treating missing labels as negative labels is one of the approaches of handling missing labels in MLC classification [45].
BR methods do not handle samples with missing labels.
Thus, we replace missing labels with negative labels and extend CC to classifier chains with a missing labels extension (CC-MLE).
Using this approach, we extend CC to handle missing labels and call it 'classifier chains with missing labels extension' (CC-MLE).
CC does not deal directly with missing labels that characterize the multi-label HTS assay datasets we have.
In the plots, abscissa labels are omitted to avoid crowding; the missing labels can be obtained by periodically replicating the shown labels.
DRABAL is a novel problem transformation MLC solution, based on inferring dependencies and handling missing labels.
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