Compared　to　conventional　machine　learning　techniques,　extreme　learning　machine　(ELM)　which　trains　single-hidden-layer　feedforward　neural　networks　(SLFNs)　shows　faster-learning　speed　and　better　generalization　performances.　However,　like　most　representative　supervised　learning　algorithms,　ELM　tends　to　produce　biased　decision　models　when　datasets　are　imbalanced.　In　this　paper,　two-stage　weighted　regularized　ELM　is　proposed　to　address　the　aforementioned　issue.　The　original　regularized　ELM　(RELM)　was　proposed　to　handle　adverse　effects　of　outliers　but　not　target　the　imbalanced　learning　problem.　So　we　proposed　a　new　weighted　regularized　ELM　(WRELM)　for　class　imbalance　learning　(CIL)　in　the　first　stage.　Different　from　the　existing　weighted　ELM　which　only　considers　the　class　distribution　of　datasets,　the　proposed　algorithm　also　puts　more　focus　on　hard,　misclassified　samples　in　the　second　stage.　The　focal　loss　function　is　adopted　to　update　weight　by　decreasing　the　weight　of　well-classified　samples　to　focus　more　attention　on　the　error　of　difficult　samples.　The　final　decision　target　is　determined　by　the　winner-take-all　method.　We　assess　the　proposed　method　on　25　binary　datasets　and　10　multiclass　datasets　by　5-folder　cross　validations.　The　results　indicate　the　proposed　algorithm　is　an　efficient　method　for　CIL　and　exceed　other　CIL　algorithms　based　on　ELM.　©　2019,　Springer　Nature　Singapore　Pte　Ltd.