To　aim　at　the　reliability　issue　of　case-based　reasoning　(CBR)　classifier,　improved　strategies　for　case　retrieve　and　case　reuse　are　introduced,　respectively.　In　the　retrieve　step,　a　new　attribute　weight　assignment　method　based　on　the　water-filling　principle　is　proposed　to　optimize　the　feature　weight;　particularly,　the　Lagrange　function　is　constructed　by　utilizing　the　mean　value　and　the　standard　deviation　of　each　attribute　to　achieve　the　weight　result,　then　a　weight　threshold　is　set　to　conduct　the　attribute　reduction.　In　the　reuse　step,　a　confidence-reuse　strategy　is　introduced　to　improve　the　efficiency　of　the　classifier　by　calculating　the　confidence　of　the　target　case　that　belongs　to　each　class.　Simulation　experiments　show　that　the　proposed　methods　could　increase　the　classification　accuracy　and　efficiency,　which　proves　that　the　improved　strategies　could　effectively　enhance　the　reliability　of　the　CBR　classifier.　Copyright　©　2014　Acta　Automatica　Sinica.　All　rights　reserved.