Contributor II

# Probabilistic Feature Selection

Hi all,

Assume I have an example set with m samples and n features.
I have weighted these n features with a statistical weighting method (like Gini, Chi-squared, InfoGain, etc.).
Now I have n normalized weighted features.
How can I probabilistically choose p features from these n features? (p << n)  // p is very smaller than n
I want each feature have a probability to be chosen amongst these p features and this probability should be its normalized weight.

Can anybody help me?
Please help me find the operator tree to solve this problem.