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Discrimination performance was assessed using a 7-fold cross-validation regime during which the classifier was trained on data from six runs and tested on the remaining.
Nonetheless, it is possible that addition of an automated feature selection step might be advantageous, particularly if it is incorporated into the cross-validation regime, i.e. if the feature selection is done separately at each LOO iteration, and/or if it is done in conjunction with feature selection based on experiments.
We used a standard k-fold cross-validation testing regime [ 15] wherein k equaled the number of experimental trials.
We used a standard k-fold cross-validation testing regime (Duda et al., 2001) wherein k equaled the number of experimental trials, with the data from each trial set aside in turn as the test data, and the remaining data used as the training set (on each fold, the feature selection step was performed using only data from this training set).
We used a standard k-fold cross-validation testing regime (Duda et al., 2001) wherein k equaled the number of experimental trials minus one (as one trial is already removed for use as the overall testing set see above), with the data from each trial set aside in turn as the test data, and the remaining data used as the training set.
What's cross validation?
Training vs. test sets and cross validation.
Westerhuis, J. A. et al. Assessment of PLSDA cross validation.
Zhang, P. Model Selection Via Multifold Cross Validation.
The method uses k-fold cross validation.
cross validation.
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