In neighborhood rough set theory, traditional heuristic algorithm for
computing reducts does not take the stability of the selected attributes
into account, it follows that the performances of the reducts may not be
good enough if the perturbations of data occur. To fill the gap, the
mechanism of acquiring the most significant attribute is realized by two
steps in the reduction process: firstly, several important attributes are
derived in each iteration based on several radii which are close to the
given radius for computing reduct; secondly, the most significant attribute
is selected from them by a voting strategy. The experiments verify that such
method can effectively improve the stabilities of the reducts, and it does
not require too much attributes for constructing the reducts.
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