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Feature selection and feature ranking are the essential components of experimental data mathematical modeling. This paper considers that a set of experimental data on an object behavior is available, where each pattern of the data set consists of input and output parts. In general, both input and output are multidimensional vectors with real values components. The paper discusses, for simplicity, a case when the input is multidimensional, while the output is one-dimensional.
We assume that input and related output are measured at equidistant moments of time 
, where n is the number of segments 
, 
is the time length of a segment. We suppose that an output y is an unknown function f of the multidimensional input 
. Uncertainty related to function f is often due to three random factors: 1) random measurement noise; 2) random maneuverings of the object; and 3) intentional hostile random influence of the object environment. By mathematical modeling we mean obtaining a statistical estimate
of the output 
which is close, in a statistical sense, to the unknown value 
of the function f at the time 
. The notation in Equation (1) means that the estimates of inputs 
are known at the time 
for inputs, if 
, and for outputs, if 
.