The　class　imbalance　problems　have　been　reported　to　severely　hinder　classification　performance　of　many　standard　learning　algorithms,　and　have　attracted　a　great　deal　of　attention　from　researchers　of　different　fields.　Therefore,　a　number　of　methods,　such　as　sampling　methods,　cost-sensitive　learning　methods,　and　bagging　and　boosting　based　ensemble　methods,　have　been　proposed　to　solve　these　problems.　However,　these　conventional　class　imbalance　handling　methods　might　suffer　from　the　loss　of　potentially　useful　information,　unexpected　mistakes　or　increasing　the　likelihood　of　overfitting　because　they　may　alter　the　original　data　distribution.　Thus　we　propose　a　novel　ensemble　method,　which　firstly　converts　an　imbalanced　data　set　into　multiple　balanced　ones　and　then　builds　a　number　of　classifiers　on　these　multiple　data　with　a　specific　classification　algorithm.　Finally,　the　classification　results　of　these　classifiers　for　new　data　are　combined　by　a　specific　ensemble　rule.　In　the　empirical　study,　different　class　imbalance　data　handling　methods　including　three　conventional　sampling　methods,　one　cost-sensitive　learning　method,　six　Bagging　and　Boosting　based　ensemble　methods,　our　previous　method　EM1vs1　and　two　fuzzy-rule　based　classification　methods　were　compared　with　our　method.　The　experimental　results　on　46　imbalanced　data　sets　show　that　our　proposed　method　is　usually　superior　to　the　conventional　imbalance　data　handling　methods　when　solving　the　highly　imbalanced　problems.　(C)　2014　Elsevier　Ltd.　All　rights　reserved.