The　extended　belief-rule-based　(EBRB)　system　has　become　a　widely　recognized　and　effective　rule-based　system　in　decision-making.　The　system　uses　a　data-driven　method　to　generate　the　rule　base　by　transforming　each　training　sample　into　a　rule.　Hence,　when　an　EBRB　system　is　applied　in　an　imbalanced　classification　dataset,　the　imbalance　of　training　dataset　will　retain　in　the　generated　rule　base.　More　specifically,　the　number　of　rules　transformed　from　majority　classes　will　be　far　greater　than　the　rules　transformed　from　minority　classes.　This　issue　usually　leads　to　a　sharp　decrease　in　the　accuracies　of　minority　classes.　This　study　analyses　how　the　imbalance　of　training　dataset　exists　in　the　generated　EBRB　and　then　proposes　a　Balance　Adjusting　(BA)　approach　to　eliminate　the　influence　of　imbalance　in　the　rule　base.　The　BA　approach　adjusts　rule　activation　weights　of　all　activated　rules,　and　further　enhances　the　competitiveness　of　rules　with　higher　activation　weight　during　the　rule　aggregation　process　of　the　EBRB　system.　Several　case　studies　in　imbalanced　benchmark　classification　datasets　from　UCI　demonstrate　how　the　use　of　the　BA　approach　improves　the　performance　of　the　EBRB　system.　This　study　also　conducts　a　series　of　experiments　to　validate　the　improvement　of　the　proposed　approach　compared　with　some　conventional　and　recent　existing　works.　The　comparison　results　illustrate　that　the　BA　approach　is　feasible,　effective　and　robust,　and　it　performs　well　especially　in　large　scale　datasets.　Moreover,　the　BA　approach　can　also　combine　with　various　rule　activation　weight　calculation　methods,　which　means　it　might　worth　to　be　applied　as　a　generic　process　before　the　rule　aggregation　process　of　the　EBRB　system.