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Can entropy be used for feature selection?
(2)The neighborhood entropy-based feature contribution is used for feature selection. It can enhance the feature selection ability.
What is entropy-based feature selection?
First, Entropy-based feature selection is based on the criteria of IG. It selects those features that provide most gain in information. For calculating information gain of features first we have to calculate probability and entropy of classes present in data set shown in Figure 5.
What are entropy features?
Entropy is shown as a potential feature for both 1D and 2D pattern recognition systems. • Entropy is shown as the outcome of a deep neural structure that fuses important information. • Feature extraction based on Entropy is described in two innovative and different ways.
How to use correlation coefficient in feature selection?
You could try to use another selection criteria for choosing between each pair of highly-correlated features. For example you can use the Information Gain (IG), which measures how much information a feature gives about the class (i.e., its reduction of entropy [TAL14], [SIL07]).
When to drop a feature with high correlation?
Correlation is a statistical term which in common usage refers to how close two variables are to having a linear relationship with each other. Features with high correlation are more linearly dependent and hence have almost the same effect on the dependent variable. So, when two features have high correlation, we can drop one of the two features.
How to measure the correlation coefficient in Python?
For example you can use the Information Gain (IG), which measures how much information a feature gives about the class (i.e., its reduction of entropy [TAL14], [SIL07]). Once you have detected a pair of highly-correlated features (e.g., as you mentioned pdays and pmonths) you can measure the IG of each variable and keep the one with the highest IG.
Which is the best method for feature selection?
F-Test captures the linear relationship well. Mutual Information captures any kind of relationship between two variables. http://scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html This method removes features with variation below a certain cutoff.