Today,　ensemble　learning　algorithms　are　proposed　to　address　the　challenges　of　high　dimensional　classification　for　steganalysis　caused　by　the　curse　of　dimensionality　and　obtain　superior　performance.　In　this　paper,　we　propose　a　classifier　ensemble　algorithm　based　on　improved　Random　Subspace　Method　(RSM)　for　high-dimensional　blind　steganalysis.　Firstly,　sequential　forward　selection　(SFS)　algorithm　is　adopted　to　select　part　of　features　with　high　classification　ability　as　fixed　subset,　so　that　the　original　feature　space　is　partitioned　into　two　parts:　fixed　subset　and　remaining　feature　subset,　then　the　final　feature　subset　is　formed　by　selecting　features　randomly　in　each　part　according　to　the　given　sampling　rate.　Secondly,　each　base　classifier　is　trained　on　the　feature　subset　and　the　weight　of　each　classifier　is　computed　according　to　the　classification　accuracy　and　mutual　information.　Finally,　the　final　decision　is　yielded　using　the　weighted　voting.　Experiments　with　the　steganographic　algorithms　HUGO　demonstrate　that　the　proposed　algorithm　can　effectively　increase　the　classification　accuracy,　in　most　cases,　the　detection　accuracy　is　better　than　RSM　and　other　classical　classifier　ensemble　methods.　©　2015.