Rule　reduction　is　one　of　the　research　objectives　in　numerous　successful　rule-based　systems.　In　some　analyses,　too　many　useless　rules　may　be　a　concern　in　a　rule-based　system.　Although　rule　reduction　has　already　attracted　wide　attention　to　optimise　the　performance　of　the　rule-based　system,　the　extended　belief-rule-based　system　(EBRBS),　which　is　an　advanced　rule-based　system　developed　from　the　belief　rule-based　system　(BRBS)　recently,　still　lacks　methods　to　reduce　rules.　This　study　focuses　on　the　rule　reduction　of　EBRBS　and　introduces　data　envelopment　analysis　(DEA)　to　evaluate　the　efficiency　of　each　rule　in　an　extended　belief-rule-based　(EBRB).　However,　two　challenges　must　be　addressed.　First,　a　measure　of　the　extended　belief　rule＇s　efficiency　value　must　be　given　because　it　is　the　foundation　of　rule　reduction.　Second,　a　novel　decision-making-unit　(DMU)　must　be　constructed　using　the　efficiency　value　of　the　extended　belief　rules　to　build　a　bridge　for　EBRBS　and　DEA.　Therefore,　the　concepts　of　contribution　degree　and　the　extended　belief　rule-based　DMU　are　introduced　in　the　present　study　for　the　first　time　to　propose　a　DEA-based　rule　reduction　method.　Moreover,　the　classic　CCR　model,　which　is　identification　engine　of　the　rule　reduction　method,　is　applied　to　calculate　the　efficiency　value　of　the　extended　belief　rule　and　finally　achieve　the　compact　structure　of　an　EBRB.　Two　case　studies　on　regression　and　classification　problems　are　performed　to　illustrate　how　efficiency　of　the　DEA-based　rule　reduction　method　in　promoting　the　performance　of　EBRBS.　Comparison　results　demonstrate　that　the　proposed　rule　reduction　can　downsize　the　EBRB　and　improve　the　accuracy　of　EBRBS.　(C)　2017　Elsevier　B.V.　All　rights　reserved.