The　distribution　of　feature　attribute　weights　and　the　strategy　of　case　retrieval　have　significant　impacts　on　the　classification　accuracy　of　case-based　reasoning　(CBR).　An　improved　CBR　classification　approach　is　proposed,　which　is　combined　with　genetic　algorithms,　introspective　learning,　and　group　decision-making　theory.　First,　multiple　attribute　weights　are　given　by　a　genetic　algorithm.　Then　each　group　of　weights　is　iteratively　adjusted　in　accordance　with　the　introspective　learning　principle.　After　that　the　group　decision-making　retrieval　result　which　satisfies　the　plurality　rule　can　be　obtained　according　to　the　case　group-retrieval　strategy.　At　last,　the　classification　comparison　experiments　prove　that　the　proposed　method　could　improve　the　classification　accuracy　of　CBR.　The　results　indicate　that　the　introspective　learning　could　guarantee　the　rationality　of　weight　allocation,　and　that　the　case　group-retrieval　strategy　could　make　full　use　of　the　potential　knowledge　of　case　base,　having　remarkable　effects　on　promoting　the　learning　ability　of　CBR.　Copyright　©　2014　Acta　Automatica　Sinica.　All　rights　reserved.