The　reasoning　ability　of　the　belief　rule-based　system　is　easy　to　be　weakened　by　the　quality　of　training　instances,　the　inconsistency　of　rules　and　the　values　of　parameters.　This　paper　proposes　an　ensemble　approach　for　extended　belief　rule-based　systems　to　address　this　issue.　The　approach　is　based　on　the　AdaBoost　algorithm　and　the　differential　evolution　(DE)　algorithm.　In　the　AdaBoost　algorithm,　the　weights　of　samples　are　updated　to　allow　the　new　subsequent　subsystem　to　pay　more　attention　to　those　samples　misclassified　by　pervious　system.　And　the　DE　algorithm　is　used　as　the　parameter　optimization　engine　to　ensure　the　reasoning　ability　of　the　learned　extended　belief　rule-based　sub-systems.　Since　the　learned　sub-systems　are　complementary,　the　reasoning　ability　of　the　belief　rule-based　system　can　be　boosted　by　combing　these　sub-systems.　Some　case　studies　about　many　classification　test　datasets　are　provided　in　this　paper　in　the　last.　The　feasibility　and　efficiency　of　the　proposed　approach　has　been　proven　by　the　experimental　results.　©　2019　The　Authors.　Published　by　Atlantis　Press　SARL.