Feature　selection　remains　a　popular　method　for　quantity　reduction　of　attributes　of　high-dimensional　data,　to　reduce　computational　costs　in　classifications.　A　new　feature　selection　method　based　on　the　joint　maximal　information　entropy　between　features　and　class　(FS-JMIE)　is　proposed　in　this　paper.　Firstly,　the　joint　maximal　information　entropy　(JMIE)　is　defined　to　measure　a　feature　subset　Next,　a　binary　particle　swarm　optimization　(BPSO)　algorithm　is　introduced　to　search　the　optimal　feature　subset.　Finally,　classification　is　performed　on　UCI　corpora　to　verify　the　performance　of　our　proposed　method　compared　to　the　traditional　mutual　information　(MI)　method,　CHI　method,　as　well　as　a　binary　version　of　particle　swarm　optimization-support　vector　machines　(BPSO-SVMs)　feature　selection.　Experiments　show　that　FS-JMIE　achieves　an　equal　or　better　performance　than　MI,　CHI,　and　BPSO-SVM.　Further,　FS-JMIE　manifests　relatively　better　robustness　to　the　number　of　classes.　Moreover,　the　method　shows　higher　consistency　and　better　time-efficiency　than　BPSO-SVM.　(C)　2017　Elsevier　Ltd.　All　rights　reserved.