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rankfeatures
'roc'
Area under the empirical receiver operating
characteristic (ROC) curve
'wilcoxon'
Absolute value of the u-statistic of a two-sample
unpaired Wilcoxon test, also known as
Mann-Whitney
Notes: 1) 'ttest', 'entropy', and 'brattacharyya' assume normal
distributed classes while 'roc' and 'wilcoxon' are nonparametric
tests. 2) All tests are feature independent.
rankfeatures(..., 'CCWeighting', ALPHA) uses correlation
information to outweigh the Z value of potential features using Z *
(1-ALPHA*(RHO)) where RHO is the average of the absolute values of
the cross-correlation coefficient between the candidate feature and all
previously selected features. ALPHA sets the weighting factor. It is a
scalar value between 0 and 1. When ALPHA is 0 (default) potential
features are not weighted. A large value of RHO (close to 1) outweighs
the significance statistic; this means that features that are highly
correlated with the features already picked are less likely to be included
in the output list.
rankfeatures(..., 'NWeighting', BETA) uses regional
information to outweigh the Z value of potential features using Z *
(1-exp(-(DIST/BETA).^2)) where DIST is the distance (in rows)
between the candidate feature and previously selected features. BETA
sets the weighting factor. It is greater than or equal to 0. When BETA is
0 (default) potential features are not weighted. A small DIST (close to 0)
outweighs the significance statistics of only close features. This means
that features that are close to already picked features are less likely
to be included in the output list. This option is useful for extracting
features from time series with temporal correlation.
BETA can also be a function of the feature location, specified using @ or
an anonymous function. In both cases rankfeatures passes the row
position of the feature to BETA() and expects back a value greater
than or equal to 0.
Note: You can use CCWeighting and NWeighting together.
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