Feature　selection　(FS)　plays　an　important　role　in　the　machine　learning　(ML)　field.　Since　FS　solves　the　problem　of　dimensional　explosion　in　ML　very　well,　more　and　more　people　are　paying　attention　to　FS.　Not　only　that,　but　this　technique　also　takes　advantage　of　the　computational　complexities　and　time　reductions.　Inspired　by　the　points　mentioned　above,　more　and　more　FS　algorithms　solved　by　deep　learning　framework　are　appearing.　Due　to　the　importance　of　FS,　it　is　necessary　to　conduct　further　research.　However,　FS　is　wide　coverage,　and　the　algorithms　involved　are　numerous,　which　makes　researchers　need　to　spend　a　lot　of　time　searching　and　reading　the　literature.　In　order　to　provide　researchers　with　dedicated　information　and　enable　them　to　quickly　have　an　overall　understanding　of　the　FS　field,　this　article　will　from　three　aspects,　including　the　main　functions　and　framework　of　FS,　search　strategies　of　FS,　and　the　evaluation　strategy　and　algorithms　in　related　fields　to　introduce　FS　from　whole　to　part.　Finally,　this　article　discusses　some　existing　problems　and　points　out　some　promising　research　directions.　©　2021,　The　Editor(s)　(if　applicable)　and　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.