Approximate reduct computation by rough sets based attribute weighting

Abstract

Rough set theory provides the reduct and the core concepts for knowledge reduction. The cost of reduct set computation is highly influenced by the attribute set size of the dataset where the problem of finding reducts has been proven as an NP-hard problem. This paper proposes an approximate approach for reduct computation. The approach uses the discernibility matrix concept and a weighting mechanism to determine the significance of an attribute to be considered in the reduct. A second supplementary weight is used to break the tie when several attributes have the same significance. The approach is extensively experimented and evaluated on various standard domains.