Contents
How mutual information can be used for feature selection?
Information gain can also be used for feature selection, by evaluating the gain of each variable in the context of the target variable. Mutual information calculates the statistical dependence between two variables and is the name given to information gain when applied to variable selection.
Why Mutual information in feature selection?
Mutual information has been successfully adopted in filter feature-selection methods to assess both the relevancy of a subset of features in predicting the target variable and the redundancy with respect to other variables.
What is mutual information in feature selection?
Joint mutual information Mutual information is a measure between two (possibly multi-dimensional) random variables X and Y, that quantifies the amount of information obtained about one random variable, through the other random variable.
What does mutual information measure?
Mutual information is one of many quantities that measures how much one random variables tells us about another. It is a dimensionless quantity with (generally) units of bits, and can be thought of as the reduction in uncertainty about one random variable given knowledge of another.
Can mutual information be negative?
applied multivariate mutual information to gene expression). It can be zero, positive, or negative.
How do you maximize mutual information?
The key idea is that maximizing mutual information between features extracted from multiple views of a shared context forces the features to capture information about higher-level factors (e.g., presence of certain objects or occurrence of certain events) that broadly affect the shared context.
How are features selected in mutual information based feature selection?
Filter methods do not use a learning algorithm on the original data X, but only consider statistical characteristics of the input data. For example, we can select the features for which the correlation between the feature and the target variable exceeds a correlation threshold.
When is mutual information known as information gain?
Mutual Information is also known as information gain . . If their joint distribution is is the Kullback–Leibler divergence . ). In general as a pair of independent random variables, when in reality they are not.
Which is a measure of joint mutual information?
Joint mutual information. Mutual information is a measure between two (possibly multi-dimensional) random variables \\(X\\) and \\(Y\\), that quantifies the amount of information obtained about one random variable, through the other random variable. The mutual information is given by
What are the values of mutual information maximisation?
Joint mutual information (JMI): α = 1 t − 1 and β = 1 t − 1. Maximum relevancy minimum redundancy (MRMR): α = 1 t − 1 and β = 0. Mutual information maximisation (MIM): α = 0 and β = 0. The different values of α and β have implications on what you’re assuming the probabilistic structure of your data looks like.