Feature　selection　has　become　a　vital　issue　in　data　mining　and　machine　learning.　But　some　challenges　have　been　outstanding　when　trying　to　improve　the　performance　of　feature　selection,　such　as　small　sample,　uncertain　classes,　complex　features,　complementation　and　redundancy　between　each　feature.　In　this　paper,　firstly　the　background　of　feature　selection　is　introduced.　Then　we　have　presented　a　new　perspective　to　analyze　multi-label　feature　selection　and　provided　typical　papers　on　different　classifications.　To　further　analyze　these　algorithms,　evaluation　criterion　on　results　of　multi-label　feature　selection　is　summarized.　Finally,　some　reflects　on　research　directions,　future　works　and　conclusions　are　organized.　©　Published　under　licence　by　IOP　Publishing　Ltd.