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- lcsi(X, y, **kwargs)
- This function implements the basic scoring criteria for linear combination of shannon information term.
The scoring criteria is calculated based on the formula j_cmi=I(f;y)-beta*sum_j(I(fj;f))+gamma*sum(I(fj;f|y))
Input
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X: {numpy array}, shape (n_samples, n_features)
input data, guaranteed to be a discrete data matrix
y: {numpy array}, shape (n_samples,)
input class labels
kwargs: {dictionary}
Parameters for different feature selection algorithms.
beta: {float}
beta is the parameter in j_cmi=I(f;y)-beta*sum(I(fj;f))+gamma*sum(I(fj;f|y))
gamma: {float}
gamma is the parameter in j_cmi=I(f;y)-beta*sum(I(fj;f))+gamma*sum(I(fj;f|y))
function_name: {string}
name of the feature selection function
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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