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- mim(X, y, **kwargs)
- This function implements the MIM feature selection
Input
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X: {numpy array}, shape (n_samples, n_features)
input data, guaranteed to be discrete
y: {numpy array}, shape (n_samples,)
input class labels
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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