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In the above algorithm, the definition of better classification performance is not reasonable.
As a result, characteristics of the EEG signals are expected to be significantly different in case of multiplication task and thus better classification performance is achieved whenever one of the two tasks to be classified is a multiplication task.
The trade-off between longer computation times associated with larger datasets and better classification performance is usually worthwhile.
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The best overall classification performance is realized when AUC is equal to 1, while an AUC of 0.5 indicates a classifier performance no better than random selection.
Hence, in the classification phase, the classification performance is improved.
Hence, although a better classification performance can be achieved, it is likely that no additional information about the more critical cases (Gleason score 7) can be obtained.
Experimental results demonstrate that better classification performance can be achieved by the proposed approach than the other state-of-the-art approaches.
In this way we think that better classification performance can be achieved than using the methods individually.
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%).
Classification performance was highly significant for all subjects.
Then the classification performance was evaluated.
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