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EEG channels that offer the best classification performance were determined.
EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined.
However, in this study, all EEG channels were evaluated and considered separately and five EEG channels that offer the best classification performance were determined.
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The concept "optimal" means the parameters with which the best classification performance is obtained.
In particular, without restricting the frequency band up to certain range, the best classification performance is achieved.
The best classification performance is obtained using the SIFT features, but a feature vector of length 128 is required for every image, and, in addition, computational space and time is needed to build internal structures (e.g. a codebook).
The best classification performance was obtained with models based on genes CD28 and CD80 together (AUC = 0.72, KStar classifier).
Under these conditions, the best classification performance was obtained with a 7-gene model (H3, GAS1, SSAT, CLU, AdoMetDC, OAZ and ODC).
The collection of all such vectors w is denoted W while the collection of vectors w with best classification performance is B. A high level depiction of wKIERA is shown in Fig. 1.
In summary, the best classification performance is achieved when luminance interval is set as 14.
The best classification performance was achieved when C P = 1 and C N = 0.6.
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