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Besides, as we find that the node identifiers are very useful features, we would like to try more effective classification models which can combine these sparse, high-dimensional and discrete features, such as node identifiers, with other deep representations in a low-dimensional space to get higher classification performance.
We show that, compared to a previously developed method, we are not only able to almost always get higher classification results in our application scenario, but that the proposed method is also able to significantly outperform the previously developed method in terms of the computational demand.
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The experiments show that the proposed algorithm gets higher classification performance and the numbers of selected features are relatively small.
We found that both LRAcluster and iCluster + got high classification accuracy for the three cancer types in the reduced low-dimension subspaces (Fig. 2a).
Deng et al. [104] showed that using ANN can get higher sensitivity and accuracy than traditional discriminant function analysis [105] in dementia classification using MRI.
It will only get higher.
Get higher than your opponent.
In terms of classification accuracy, the semi-supervised classification algorithms TCA, LapRLS/L, SDA get a higher classification accuracy than the supervised classification algorithm MFA.
Note that MetaCluster 5.0 makes an effort to get high precision by using the techniques of removing extremely low-coverage reads from classification process and generating more clusters if needed.
They get high numbers.
They get high.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com