Feature　selection　is　viewed　as　an　important　preprocessing　step　for　pattern　recognition,　machine　learning　and　data　mining.　Considering　a　consistency　measure　introduced　in　rough　sets,　the　problem　of　feature　selection　aims　to　retain　the　discriminatory　power　of　original　features.　Many　heuristic　feature　selection　algorithms　have　been　proposed,　however,　these　methods　are　computationally　time-consuming.　This　paper　introduces　granular　space,　positive　granular　space　and　negative　granular　space　based　on　granular　computing　in　simplified　decision　systems,　and　then　new　feature　significance　measure　is　proposed.　Meanwhile,　their　important　propositions　and　properties　are　derived.　Furthermore,　by　virtue　of　radix　sorting　and　Hash　techniques,　the　object　granules　as　basic　processing　elements　are　employed　to　investigate　feature　selection,　and　then　a　heuristic　algorithm　with　low　computational　complexity　is　explored.　Numerical　simulation　experiments　show　that　the　proposed　approach　is　indeed　efficient,　and　therefore　of　practical　value　to　many　real-world　problems.　©　2013　ACADEMY　PUBLISHER.