Data-driven　modeling　based　on　the　shell　vibration　and　acoustic　signals　of　ball　mills　is　normally　applied　to　overcome　the　subjective　errors　of　human　inference.　Many　previously　proposed　selective　ensemble　(SEN)　modeling　approaches　are　based　on　＂the　manipulation　of　input　features＂　from　the　multiinformation　fusion　perspective,　which　cannot　selectively　and　jointly　fuse　the　information　hidden　in　multiscale　spectral　features　and　under　several　operating　conditions　(training　samples).　Therefore,　this　study　suggests　a　new　soft　measuring　procedure　based　on　ensemble　empirical　mode　decomposition　(EEMD)　and　SEN.　An　improved　kernel　partial　least-squares　algorithm　for　SEN　that　is　based　on　＂subsample　training　samples＂　is　utilized　to　construct　a　soft　measuring　model　with　the　selected　features　and　training　samples.　This　study　compares　such　data-driven　soft　measuring　methods.　The　comparative　results　of　bootstrap-based　prediction　performance　estimation　show　that　different　methods　have　specific　advantages　in　terms　of　simplicity,　prediction　accuracy,　and　interpretability.　The　industrial　application　of　the　EEMD-SEN　method　is　discussed　in　this　paper,　and　a　new　virtual　sample　generationmethod　is　proposed　to　address　the　modeling　problem　based　on　small　sample　spectral　data.