Selecting　relevant　features　is　in　demand　when　a　large　data　set　is　of　interest　in　a　classification　task.　It　produces　a　tractable　number　of　features　that　are　sufficient　and　possibly　improve　the　classification　performance.　This　paper　studies　a　statistical　method　of　Markov　blanket　induction　algorithm　for　filtering　features　and　then　applies　a　classifier　using　the　Markov　blanket　predictors.　The　Markov　blanket　contains　a　minimal　subset　of　relevant　features　that　yields　optimal　classification　performance.　We　experimentally　demonstrate　the　improved　performance　of　several　classifiers　using　a　Markov　blanket　induction　as　a　feature　selection　method.　In　addition,　we　point　out　an　important　assumption　behind　the　Markov　blanket　induction　algorithm　and　show　its　effect　on　the　classification　performance.