Parameters for Feature Selection

1. Wrapper approach : This approach has high computational complexity. It uses a learning algorithm to evaluate the accuracy produced by the use of the selected features in classification. Wrapper methods can give high classification accuracy for particular classifiers.
2. Filter approach : A subset of features is selected by this approach without using any learning algorithm. Higher-dimensional datasets use this method and it is relatively faster than the wrapper-based approaches.
3. Embedded approach : The applied learning algorithms determine the specificity of this approach and it selects the features during the process of training the data set.
4. Hybrid approach : Both filter and wrapper-based methods are used in hybrid approach. This approach first selects the possible optimal feature set which is further tested by the wrapper approach. It hence uses the advantages of both filter and wrapper-based approach.

Parameters For Feature Selection :

where 
cov(X, Y) - covariance
sigma(X) - standard deviation of X
sigma(Y) - standard deviation of Y

where
X - discrete random variable X
P(X) - probability mass function
E - expected value operator, 
I - information content of X.
I(X) - a random variable.

where 
p(x, y) - joint probability function of X and Y,
p(x) - marginal probability distribution function of X
p(y) - marginal probability distribution function of Y