Recently,　restricted　Boltzmann　machines　(RBMs)　have　attracted　considerable　interest　in　machine　learning　field　due　to　their　strong　ability　to　extract　features.　Given　some　training　data,　an　RBM　or　a　stack　of　several　RBMs　can　be　used　to　extract　informative　features.　Meanwhile,　ensemble　learning　is　an　active　research　area　in　machine　learning　owing　to　their　potential　to　greatly　increase　the　prediction　accuracy　of　a　single　classifier.　However,　RBMs　have　not　been　studied　to　work　with　ensemble　learning　so　far.　In　this　study,　we　present　several　methods　for　integrating　RBMs　with　bagging　to　generate　diverse　and　accurate　individual　classifiers.　Taking　a　classification　tree　as　the　base　learning　algorithm,　a　thoroughly　experimental　study　conducted　on　31　real-world　data　sets　yields　some　promising　conclusions.　When　using　the　features　extracted　by　RBMs　in　ensemble　learning,　the　best　way　is　to　perform　model　combination　respectively　on　the　original　feature　set　and　the　one　extracted　by　a　single　RBM.　However,　the　prediction　performance　becomes　worse　when　the　features　detected　by　a　stack　of　2　RBMs　are　also　considered.　As　for　the　features　detected　by　RBMs,　good　classification　can　be　obtained　only　when　they　are　used　together　with　the　original　features.　(C)　2013　Elsevier　B.V.　All　rights　reserved.