Hacibeyoglu, MehmetBasciftci, FatihKahramanli, Sirzat2020-03-262020-03-2620111300-0632https://dx.doi.org/10.3906/elk-1008-726https://hdl.handle.net/20.500.12395/26057The goal of attribute reduction is to find a minimal subset (MS) R of the condition attribute set C of a dataset such that R has the same classification power as C. It was proved that the number of MSs for a dataset with n attributes may be as large as ((n)(n/2)) and the generation of all of them is an NP-hard problem. The main reason for this is the intractable space complexity of the conversion of the discernibility function (DF) of a dataset to the disjunctive normal form (DNF). Our analysis of many DF-to-DNF conversion processes showed that approximately (1 - 2/((n)(n/2)) x 100) % of the implicants generated in the DF-to-DNF process are redundant ones. We prevented their generation based on the Boolean inverse distribution law. Due to this property, the proposed method generates 0.5 x ((n)(n/2)) times fewer implicants than other Boolean logic-based attribute reduction methods. Hence, it can process most of the datasets that cannot be processed by other attribute reduction methods.en10.3906/elk-1008-726info:eu-repo/semantics/closedAccessInformation systemdatasetattribute reductionfeature selectiondiscernibility functioncomputational complexityreductArticle194643656Q3WOS:000292016500010Q4