Binary　relevance　(BR),　a　basic　Multi-label　classification　(MLC)　method,　learns　a　single　binary　model　for　each　different　label　without　considering　the　dependences　among　rest　of　labels.　Many　chaining　and　stacking　techniques　exploit　the　dependences　among　labels　to　improve　the　predictive　accuracy　for　MLC.　Using　the　se　two　techniques,　BR　has　been　promoted　as　dependent　binary　relevance　(DBR).　In　this　paper　we　propose　a　pruning　method　for　DBR,　in　which　the　Phi　coefficient　function　has　been　employed　to　estimate　correlation　degrees　among　labels　for　removing　irrelevant　labels.　We　conducted　our　pruning　algorithm　on　benchmark　multi-label　datasets,　and　the　experimental　results　show　that　our　pruning　approach　can　reduce　the　computational　cost　of　DBR　and　improve　the　predictive　performance　generally.