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- cmim(X, y, **kwargs)
- This function implements the CMIM feature selection.
The scoring criteria is calculated based on the formula j_cmim=I(f;y)-max_j(I(fj;f)-I(fj;f|y))
Input
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X: {numpy array}, shape (n_samples, n_features)
Input data, guaranteed to be a discrete numpy array
y: {numpy array}, shape (n_samples,)
guaranteed to be a numpy array
kwargs: {dictionary}
n_selected_features: {int}
number of features to select
Output
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F: {numpy array}, shape (n_features,)
index of selected features, F(1) is the most important feature
Reference
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Brown, Gavin et al. "Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection." JMLR 2012.
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