This　paper　presents　a　generalized　pruning　extreme　learning　machine　(GP-ELM)　algorithm　which　can　generate　a　compact　single-hidden-layer　neural　network　(SLNN)　by　automatically　pruning　the　number　of　hidden　nodes　iteratively　while　keep　high　accuracy.　The　proposed　GP-ELM　algorithm　initializes　a　SLNN　by　using　extreme　learning　algorithm　(ELM)　algorithm　given　superfluous　number　of　hidden　nodes.　The　following　of　GP-ELM　algorithm　consists　of　two　iterative　processes.　First,　the　significance　of　each　hidden　nodes　is　estimated　and　the　insignificant　nodes　are　removed.　Secondly,　the　existing　hidden　nodes　are　updated　by　using　ELM　algorithm.　These　two　processes　continue　until　the　stop　condition　is　satisfied　such　that　a　reasonably　compact　network　is　achieved.　Compared　against　other　state-of-　The-art　algorithms,　GP-ELM　algorithm　has　mainly　two　improvements.　First,　a　supervised　training　process　is　integrated　into　the　pruning　process　such　that　significance　of　each　hidden　node　can　be　estimated　more　precisely　in　the　next　iteration.　Secondly,　the　pruning　threshold　is　set　based　on　the　input　data　dimension　such　that　the　threshold　can　accommodate　any　data　type.　Experimental　results　have　shown　that　the　GP-ELM　algorithm　can　automatically　achieve　a　reasonable　compact　network　structure　while　keep　comparable　or　much　higher　accuracy　in　classification　and　regression.　©　2015　IEEE.