Random　subspace　method　(RSM),　which　randomly　selects　low　dimensional　feature　subspace　from　the　original　high　dimensional　feature　space　to　form　new　training　subsets,　is　an　effective　ensemble　learning　method　for　high　dimensional　samples.　However,　RSM　also　has　the　drawbacks:　Random　selection　of　features　does　not　guarantee　that　the　selected　inputs　have　the　necessary　discriminant　information.　If　such　is　the　case,　poor　classifiers　are　obtained　that　damage　the　ensemble.　Thus,　we　put　forward　a　selective　ensemble　RSM　method　Based　on　FP-Tree.　The　method　obtains　a　refined　transaction　database　and　builds　a　FP-Tree　to　compact　it,　next,　selects　an　ensemble　size　according　to　the　FP-Tree.　Since　the　proposed　method　only　selects　part　of　classifiers　to　ensemble　which　can　eliminate　the　poor　individual　classifiers　and　obtain　better　ensemble　results　than　using　all　the　base　classifiers.　We　utilize　the　proposed　method　to　fight　against　the　newly　proposed　HUGO　steganographic　algorithm.　Experiment　results　show　that　our　method　has　the　overall　best　detection　performance.　©　2013　IEEE.