In　order　to　obtain　the　minimal　reduction　of　decision　table　attributes,　an　attribute　reduction　algorithm　is　proposed　based　on　ant　colony　optimization.　The　significance　of　attributes　defined　from　the　viewpoint　of　information　theory　is　used　as　the　heuristic　information.　The　algorithm　directly　imports　the　core　into　each　solution　constructed　by　ants　and　reduces　the　problem　scale.　The　new　state　transition　rule　and　pheromone　updating　rule　reflects　the　orderless　characteristic　among　attributes,　and　benefits　the　search　in　the　neighborhood　of　good　solutions.　The　algorithm　is　verified　on　nine　typical　instances.　Experimental　results　show　that,　compared　with　the　existing　algorithms,　the　proposed　algorithm　can　find　the　minimal　reduction　more　easily　with　less　time.