This　paper　proposes　a　learning　algorithm　called　Semi-supervised　Online　Sequential　ELM,　denoted　as　SOS-ELM.　It　aims　to　provide　a　solution　for　streaming　data　applications　by　learning　from　just　the　newly　arrived　observations,　called　a　chunk.　In　addition,　SOS-ELM　can　utilize　both　labeled　and　unlabeled　training　data　by　combining　the　advantages　of　two　existing　algorithms:　Online　Sequential　ELM　(OS-ELM)　and　Semi-Supervised　ELM　(SS-ELM).　The　rationale　behind　our　algorithm　exploits　an　optimal　condition　to　alleviate　empirical　risk　and　structure　risk　used　by　SS-ELM,　in　combination　with　block　calculation　of　matrices　similar　to　OS-ELM.　Efficient　implementation　of　the　SOS-ELM　algorithm　is　made　viable　by　an　additional　assumption　that　there　is　negligible　structural　relationship　between　chunks　from　different　times.　Experiments　have　been　performed　on　standard　benchmark　problems　for　regression,　balanced　binary　classification,　unbalanced　binary　classification　and　multi-class　classification　by　comparing　the　performance　of　the　proposed　SOS-ELM　with　OS-ELM　and　SS-ELM.　The　experimental　results　show　that　the　SOS-ELM　outperforms　OS-ELM　in　generalization　performance　with　similar　training　speed,　and　in　addition　outperforms　SS-ELM　with　much　lower　supervision　overheads.　(C)　2015　Elsevier　B.V.　All　rights　reserved.