Scalable Feature Selection Using Rough Set Theory
نویسندگان
چکیده
In this paper, we address the problem of feature subset selection using rough set theory. We propose a scalable algorithm to find a set of reducts based on discernibility function, which is an alternative solution for the exhaustive approach. Our study shows that our algorithm improves the classical one from three points of view: computation time, reducts size and the accuracy of induced model.
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