  Identification of informative variables in an information system is often
performed using simple one-dimensional filtering procedures that discard
information about interactions between variables. Such approach may result in
removing some relevant variables from consideration. Here we present an R
package MDFS (MultiDimensional Feature Selection) that performs identification
of informative variables taking into account synergistic interactions between
multiple descriptors and the decision variable. MDFS is an implementation of an
algorithm based on information theory. Computational kernel of the package is
implemented in C++. A high-performance version implemented in CUDA C is also
available. The applications of MDFS are demonstrated using the well-known
Madelon dataset that has synergistic variables by design. The dataset comes
from the UCI Machine Learning Repository. It is shown that multidimensional
analysis is more sensitive than one-dimensional tests and returns more reliable
rankings of importance.
