Extreme　learning　machine　(ELM)　is　an　efficient　learning　algorithm　which　can　be　easily　used　with　least　human　intervene.　But　when　ELM　is　applied　as　multiclass　classifier,　the　results　of　some　classes　are　not　satisfactory　and　it＇s　hard　to　adjust　the　parameters　for　these　classes　without　affecting　other　classes.　To　overcome　these　limitations,　a　novel　method　is　proposed.　In　proposed　approach,　binary　ELM　classifiers　for　each　class　are　combined　into　an　ensemble　classifier　using　one-to-all　strategy.　The　experiment　on　NSL-KDD　data　shows　that　the　proposed　approach　outperforms　ELM　multiclass　classifier,　decision　tree,　neural　network　(NN)　and　support　vector　machines　(SVM).　©　2015　IEEE.