In　wireless　sensor　networks,　collected　data　usually　have　a　certain　degree　of　loss　and　are　unable　to　meet　actual　application　needs　due　to　node　failures　or　energy　limitation,　etc.　The　current　data　recovery　methods　in　wireless　sensor　networks　focus　on　the　usage　of　spatial–temporal　correlation　between　perceptual　data　but　seldom　exploit　the　correlation　between　different　attributes.　This　paper　proposes　a　data　recovery　algorithm　based　on　the　Attribute　Correlation　and　Extremely　randomized　Trees　(ACET).　Firstly,　the　Spearman’s　correlation　coefficient　is　adopted　to　construct　the　correlation　model　between　different　attributes.　In　case　that　a　given　attribute　is　lost,　the　correlation　model　is　used　to　select　other　attributes　that　have　a　strong　correlation　with　this　attribute,　and　then　take　advantage　of　them　to　train　the　extremely　randomized　trees.　Finally,　the　lost　data　can　be　recovered　by　the　trained　model.　Experimental　results　show　that　the　correlation　between　attributes　can　improve　the　effectiveness　of　data　recovery　compared　with　other　methods.　©　2019,　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.