Class　imbalance　is　a　common　problem　in　real-world　applications　and　usually　poses　a　major　challenge　to　artificial　intelligent　(AI)-based　decision　models.　The　present　work　introduces　a　novel　ensemble　decision　model　which　utilizes　an　explainable　and　fast-growing　rule-based　system,　called　extended　belief　rule　base　(EBRB)　decision　model,　to　alleviate　the　impact　of　class　imbalance,　where　the　proposed　ensemble　EBRB　model　includes　two　core　components:　a　diversity-based　base　EBRB　construction　scheme　and　a　consistency-based　ensemble　EBRB　inference　scheme.　Specifically,　for　the　purpose　of　enhancing　diversity　in　the　construction　scheme,　various　kinds　of　oversampling　techniques　are　applied　to　construct　diverse　base　EBRBs　firstly,　followed　by　the　calculation　of　attribute　weights　based　on　information　gain.　As　for　the　inference　scheme,　the　proposed　ensemble　EBRB　model　aims　to　produce　inferential　outputs　not　only　integrating　the　rules　activated　from　all　base　EBRBs,　but　also　taking　into　consideration　the　consistency　of　the　activated　rules.　In　experimental　study,　twenty-six　imbalanced　classification　datasets　are　used　to　demonstrate　the　effectiveness　of　the　proposed　ensemble　EBRB　decision　model.　Results　demonstrate　that　the　proposed　model　outperforms　conventional　EBRB　systems　and　other　typical　imbalanced　classifiers.　©　2022　Elsevier　B.V.