In　recent　years,　data　imbalance　in　the　conventional　classification　problem　has　raised　great　interest　in　the　industry.　However,　concerning　the　rule-based　systems,　this　problem　has　been　rarely　investigated.　We　propose　a　Belief　Rule-Based　(BRB)　reasoning　model　based　on　the　evidential　reasoning　algorithm　to　fill　in　the　gap　by　solving　the　problem　caused　by　imbalanced　data　with　regards　to　the　rule-based　systems.　It　utilizes　the　data-driven　characteristics　of　Extended　Belief　Rule-Based　(EBRB)　and　incorporates　the　data　class　rebalancing　technique　with　the　ensemble　learning　method　of　EBRB.　In　order　to　increase　the　number　of　rules　that　can　promote　the　correct　classification　of　minority　classes　in　EBRB,　we　apply　an　adaptive　data　resampling　strategy　to　expand　the　proportion　of　minority　classes.　On　this　basis,　the　data　is　divided　into　several　EBRB　base　learners　with　different　classification　characteristics.　During　the　iterative　process,　the　rule　weight　of　the　base　learner　is　dynamically　adjusted　according　to　the　correct　and　incorrect　classification,　such　that　the　classifier　can　pay　more　attention　to　the　minority　classes　and　reduce　the　bias　of　classification.　Finally,　the　optimized　EBRB　base　learner　is　selected　to　build　an　ensemble　classifier　with　excellent　classification　performance.　Experiments　on　27　binary　class　and　12　multi-class　imbalanced　datasets　show　that　our　approach　significantly　improves　F-value　and　MACC　compared　with　other　imbalanced　classification　algorithms.　By　building　a　cascaded　EBRB　model,　the　activation　weight　is　taken　as　the　basis　of　model　evolution,　the　application　limitations　of　the　EBRB　model　in　the　ensemble　learning　are　solved　innovatively,　and　the　classification　performance　of　the　EBRB　model　for　imbalanced　data　is　improved　significantly.　©　2021　Elsevier　B.V.