A Method For Finding Minimal Sets Of
Features Adequately Describing Discrete
Information Objects
Author(s)
D. Sitnikov, O. Titova, O. Romanenko & O. Ryabov
Abstract
One of the classical Data Mining problems is the problem of classifying new
objects on the basis of available information when the information associated
with these objects does not allow identifying them unambiguously as elements of
some set. In such cases using rough sets theory is often an effective solution.
This theory operates with such concepts as \“indiscernible” elements and
relations. A rough set is characterized by lower and upper approximations for
finding which the authors earlier suggested an original algebraic method. The
given method uses only logic operations, which makes the process of searching
logic rules very quick and efficient.
The upper and lower approximations of a rough set allow describing elements
of this set as completely as it is possible from the viewpoint of available
information. In this connection it seems interesting and important to find
irreducible sets of features describing a rough set with the same \“precision” as
with the help of a full set of features (so called reducts). This problem is quite
difficult and complicated and at present it does not have good solutions. Our
paper continues research carried out by the authors earlier and we suggest a
method for finding reducts based on eliminating non-salient features in the
reverse order of their importance. The suggested procedure allows us to avoid
exhaustive searching by extracting a predefined number of most significant
reducts. In this paper we consider arbitrary features taking on their values from
finite sets.
Keywords: rough set, low approximation, upper approximation, boundary
region, reduct.
Keywords
rough set, low approximation, upper approximation, boundary
region, reduct.
Related Book
Other papers in this volume
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