Dioxin　(DXN),　which　is　named　a　＇century　poison＇,　is　emitted　from　municipal　solid　waste　incineration　(MSWI).　The　first　step　to　effectively　control　and　reduce　DXN　emissions　is　the　application　of　soft　sensors　by　utilizing　easy-to-detect　process　data.　However,　DXN　samples　for　data-driven　modeling　are　extremely　lacking　because　of　the　high　cost　and　long　period　of　measurement.　To　address　the　above　issue,　this　work　proposes　a　DXN　emission　prediction　method　based　on　expansion,　interpolation,　and　selection　for　small-sample　modeling,　i.e.,　EIS-SSM,　involving　three　main　steps:　domain　expansion,　hybrid　interpolation,　and　virtual　sample　selection.　First,　the　domain　of　samples　is　determined　by　domain　extension,　a　great　number　of　virtual　samples　in　this　domain　are　generated　through　hybrid　interpolation,　and　the　optimal　virtual　samples　are　chosen　for　virtual　sample　selection.　Afterward,　a　prediction　model　for　DXN　emission　is　constructed　using　the　optimal　samples　and　raw　small　samples.　Two　cases,　that　is,　a　benchmark　dataset　and　a　DXN　dataset　from　an　actual　MSWI　plant,　are　applied　to　implement　the　proposed　method.　Results　showed　that　compared　with　the　non-expansion　and　existing　expansion　methods,　the　proposed　method　exhibits　an　improved　performance　by　48.22%　and　13.68%,　respectively,　in　the　benchmark　experiment　and　by　72.44%　and　34.67%,　respectively,　in　the　DXN　emission　prediction　experiment.　Therefore,　the　proposed　method　can　substantially　improve　the　prediction　of　DXN　emission　from　MSWI.　©　2022　Elsevier　Ltd.