Feature　selection　(FS)　plays　an　important　role　in　machine　learning.　FS　under　minimum　redundancy　maximum　relevance　framework　based　on　mutual　information　behaved　well　according　to　existing　researched.　This　paper　focus　on　the　validity　of　the　MM-Redundancy　Max　-Relevance　(mRMR)　framework　with　some　traditional　correlative　criteria,　such　as　Spearman　coefficient,　distance　correlation　(dCor),　and　maximal　information　coefficient　(MIC),　etc.　Experimental　results　show　that　mRMR　can　bring　encouraging　feature　selection　result　compared　with　the　traditional　K-BEST　feature　selection　method,　no　matter　which　criterion　is　adopted　and　the　classification　accuracy　of　these　criteria　is　improved　under　the　mRMR　framework.