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A rigorous 10-fold cross validation test is performed to test the classification performance.
The summary of the classification performance was subsequently assessed in the group-level analysis to determine whether the classification performance was above the chance level (i.e., 25%).
Then, the classification performance on them has been investigated as a further separate test.
The classification performance of the methods is presented.
Results show that data preprocessing can change the classification performance.
A direct optimization to the classification performance is thus desired.
This inconsistency will then reduce the classification performance.
Again, AFs found by EDS improve the classification performance.
This improves the classification performance with reducing training time.
Hence, in the classification phase, the classification performance is improved.
The classification performance is examined on the holdout dataset.
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CEO of Professional Science Editing for Scientists @ prosciediting.com