DISR: Double input symmetrical relevance filter

View source: R/algorithms.R

DISRR Documentation

Double input symmetrical relevance filter


The method starts with a feature of a maximal mutual information with the decision Y. Then, it greedily adds feature X with a maximal value of the following criterion:

J(X)=∑_{W\in S} \frac{I(X,W;Y)}{H(X,W,Y)},

where S is the set of already selected features.


DISR(X, Y, k = 3, threads = 0)



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). Single vector will be interpreted as a data.frame with one column. NAs are not allowed.


Decision attribute; should be given as a factor, but other options are accepted, exactly like for attributes. NAs are not allowed.


Number of attributes to select. Must not exceed ncol(X).


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 be at most of a length k, as the selection may stop sooner, even during initial selection, in which case both vectors will be empty.


DISR is a normalised version of JMI; JMIM and NJMIM are modifications of JMI and DISR in which minimal joint information over already selected features is used instead of a sum.

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 non-factor vectors in inputs, which requires additional time, memory 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 equally-spaced 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 n-level categorical, which would have a perfect mutual information score and would likely become a very disruptive false positive.


"On the Use of Variable Complementarity for Feature Selection in Cancer Classification" P. Meyer and G. Bontempi, (2006)



praznik documentation built on May 20, 2022, 5:06 p.m.