Feature　subset　selection　is　an　efficient　step　to　reduce　the　dimension　of　data,　which　remains　an　active　research　field　in　decades.　In　order　to　develop　highly　accurate　and　fast　searching　feature　subset　selection　algorithms,　a　filter　feature　subset　selection　method　combining　maximal　information　entropy　(MIE)　and　the　maximal　information　coefficient　(MIC)　is　proposed　in　this　paper.　First,　a　new　metric　mMIE-mMIC　is　defined　to　minimize　the　MIE　among　features　while　maximizing　the　MIC　between　the　features　and　the　class　label.　The　mMIE-mMIC　algorithm　is　designed　to　evaluate　whether　a　candidate　subset　is　valid　for　classification.　Second,　two　searching　strategies　are　adopted　to　identify　a　suitable　solution　in　the　candidate　subset　space,　including　the　binary　particle　swarm　optimization　algorithm　(BPSO)　and　sequential　forward　selection　(SFS).　Finally,　classification　is　performed　on　UCI　datasets　to　validate　the　performance　of　our　work　compared　to　9　existing　methods.　Experimental　results　show　that　in　most　cases,　the　proposed　method　behaves　equally　or　better　than　the　other　9　methods　in　terms　of　classification　accuracy　and　F1-score.