Feature　selection　is　one　of　the　major　aspects　of　pattern　classification　systems.　In　previous　studies,　Ding　and　Peng　recognized　the　importance　of　feature　selection　and　proposed　a　minimum　redundancy　feature　selection　method　to　minimize　redundant　features　for　sequential　selection　in　microarray　gene　expression　data.　However,　since　the　minimum　redundancy　feature　selection　method　is　used　mainly　to　measure　the　dependency　between　random　variables　of　mutual　information,　the　results　cannot　be　optimal　without　consideration　of　global　feature　selection.　Therefore,　based　on　the　framework　of　minimum　redundancy-maximum　correlation,　this　paper　introduces　entropy　to　measure　global　feature　selection　and　proposes　a　new　feature　subset　evaluation　method,　differential　correlation　information　entropy.　In　our　function,　different　bivariate　correlation　metrics　are　selected.　Then,　the　feature　selection　is　completed　through　sequence　forward　search.　Two　different　classification　models　are　used　on　eleven　standard　data　sets　of　the　UCI　machine　learning　knowledge　base　to　compare　various　comparison　algorithms,　such　as　mRMR,　reliefF　and　feature　selection　method　with　joint　maximal　information　entropy,　with　our　method.　The　experimental　results　show　that　feature　selection　based　on　our　proposed　method　is　obviously　superior　to　that　of　other　models.