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- cfs(X, y)
- This function uses a correlation based heuristic to evaluate the worth of features which is called CFS
Input
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X: {numpy array}, shape (n_samples, n_features)
input data
y: {numpy array}, shape (n_samples,)
input class labels
Output
------
F: {numpy array}
index of selected features
Reference
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Zhao, Zheng et al. "Advancing Feature Selection Research - ASU Feature Selection Repository" 2010.
- merit_calculation(X, y)
- This function calculates the merit of X given class labels y, where
merits = (k * rcf)/sqrt(k+k*(k-1)*rff)
rcf = (1/k)*sum(su(fi,y)) for all fi in X
rff = (1/(k*(k-1)))*sum(su(fi,fj)) for all fi and fj in X
Input
----------
X: {numpy array}, shape (n_samples, n_features)
input data
y: {numpy array}, shape (n_samples,)
input class labels
Output
----------
merits: {float}
merit of a feature subset X
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