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In the feature selection step, we set the best informative features penalty-free in the generalized lasso method.
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Finding these more informative features can best be done using local measures of electrophysiological activity as icEEG, and further investigating the colocalised haemodynamic fluctuations.
It ranks features according to certain information criteria and only the most informative features are used for classification.
In the feature ranking step, all features are ranked by their MI scores, and a subset of high-ranked features are selected and considered to have the best informative power.
The final ranking of all features does not give the information about the cut-off value that separates informative features from the non-informative ones.
We consider shadow as informative features in good weather.
Then, the most informative features should be selected.
The proposed automatic scheme is noticed to retaining informative features or eliminating redundant features simultaneously.
And like stationarity, nonstationarity also carries informative features of the channel.
Many efforts have focused on studying techniques for selecting most informative features from data sets.
The enormous brain signal data measured by fNIRS are reduced by proposed feature selection and extracted the informative features.
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