Description Usage Arguments Value Note References Examples
The method starts with an attribute of a maximal mutual information with the decision Y. Then, it greedily adds attribute X with a maximal value of the following criterion:
J(X)=I(X;Y)\frac{1}{S}∑_{W\in S} I(X;W),
where S is the set of already selected attributes.
1  MRMR(X, Y, k = 3, threads = 0)

X 
Attribute table, given as a data frame with either factors (preferred), booleans, integers (treated as categorical) or reals (which undergo automatic categorisation; see below for details).

Y 
Decision attribute; should be given as a factor, but other options are accepted, exactly like for attributes.

k 
Number of attributes to select.
Must not exceed 
threads 
Number of threads to use; default value, 0, means all available to OpenMP. 
A list with two elements: selection
, a vector of indices of the selected features in the selection order, and score
, a vector of corresponding feature scores.
Names of both vectors will correspond to the names of features in X
.
Both vectors will either have a length k
or zero, when all features turn out to have zero mutual information with the decision.
The method requires input to be discrete to use empirical estimators of distribution, and, consequently, information gain or entropy.
To allow smoother user experience, praznik automatically coerces nonfactor vectors in X
and Y
, which requires additional time and space and may yield confusing results – the best practice is to convert data to factors prior to feeding them in this function.
Real attributes are cut into about 10 equallyspaced bins, following the heuristic often used in literature.
Precise number of cuts depends on the number of objects; namely, it is n/3, but never less than 2 and never more than 10.
Integers (which technically are also numeric) are treated as categorical variables (for compatibility with similar software), so in a very different way – one should be aware that an actually numeric attribute which happens to be an integer could be coerced into a nlevel categorical, which would have a perfect mutual information score and would likely become a very disruptive false positive.
"Feature Selection Based on Mutual Information: Criteria of MaxDependency, MaxRelevance, and MinRedundancy" H. Peng et al. IEEE Pattern Analysis and Machine Intelligence (PAMI) (2005)
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