Feature　selection　is　a　key　problem　in　tumor　classification　and　related　tasks.　This　paper　presents　a　tumor　classification　approach　with　neighborhood　rough　set-based　feature　selection.　First,　some　uncertainty　measures　such　as　neighborhood　entropy,　conditional　neighborhood　entropy,　neighborhood　mutual　information　and　neighborhood　conditional　mutual　information,　are　introduced　to　evaluate　the　relevance　between　genes　and　related　decision　in　neighborhood　rough　set.　Then　some　important　properties　and　propositions　of　these　measures　are　investigated,　and　the　relationships　among　these　measures　are　established　as　well.　By　using　improved　minimal-Redundancy-Maximal-Relevancy,　combined　with　sequential　forward　greedy　search　strategy,　a　novel　feature　selection　algorithm　with　low　time　complexity　is　proposed.　Finally,　several　cancer　classification　tasks　are　demonstrated　using　the　proposed　approach.　Experimental　results　show　that　the　proposed　algorithm　is　efficient　and　effective.