Classification　problems　are　significant　because　they　constitute　a　meta-model　for　multiple　theoretical　and　practical　applications　from　a　wide　range　of　fields.　The　belief　rule　based　(BRB)　expert　system　has　shown　potentials　in　dealing　with　both　quantitative　and　qualitative　information　under　uncertainty.　In　this　study,　a　BRB　classifier　is　proposed　to　solve　the　classification　problem.　However,　two　challenges　must　be　addressed.　First,　the　size　of　the　BRB　classifier　must　be　controlled　within　a　feasible　range　for　better　expert　involvement.　Second,　the　initial　parameters　of　the　BRB　classifier　must　be　optimized　by　learning　from　the　experts＇　knowledge　and/or　historic　data.　Therefore,　new　rule　activation　and　weight　calculation　procedures　are　proposed　to　downsize　the　BRB　classifier　while　maintaining　the　matching　degree　calculation　procedure.　Moreover,　the　optimal　algorithm　using　the　evidential　reasoning　(ER)　algorithm　as　the　inference　engine　and　the　differential　evolution　(DE)　algorithm　as　the　optimization　engine　is　proposed　to　identify　the　fittest　parameters,　including　the　referenced　values　of　the　antecedent　attributes,　the　weights　of　the　rules　and　the　beliefs　of　the　degrees　in　the　conclusion.　Five　benchmarks,　namely,　iris,　wine,　glass,　cancer　and　pima,　are　studied　to　validate　the　efficiency　of　the　proposed　BRB　classifier.　The　result　shows　that　all　five　benchmarks　could　be　precisely　modeled　with　a　limited　number　of　rules.　The　proposed　BRB　classifier　has　also　shown　superior　performance　in　comparing　it　with　the　results　in　the　literature.　©　2015　Elsevier　Inc.　All　rights　reserved.