Kernel-based　modeling　methods　have　been　used　widely　to　estimate　some　difficulty-to-measure　quality　or　efficient　indices　at　different　industrial　applications.　Least　square　support　vector　machine　(LSSVM)　is　one　of　the　popular　ones.　However,　its　learning　parameters,　i.e.,　kernel　parameter　and　regularization　parameter,　are　sensitive　to　the　training　data　and　the　model＇s　prediction　performance.　Ensemble　modeling　method　can　improve　the　generalization　performance　and　reliability　of　the　soft　measuring　model.　Aim　at　these　problems,　a　new　adaptive　selective　ensemble　(SEN)　LSSVM　(SEN-LSSVM)　algorithm　is　proposed　by　using　multiple　learning　parameters.　Candidate　regularization　parameters　and　candidate　kernel　parameters　are　used　to　construct　many　of　candidate　sub-sub-models　based　on　LSSVM.　These　sub-sub-models　based　on　the　same　kernel　parameter　are　selected　and　combined　as　candidate　SEN-sub-models　by　using　branch　and　bound　based　SEN　(BBSEN).　By　employing　BBSEN　at　the　second　time,　these　SEN-sub-models　based　on　different　kernel　parameters　are　used　to　obtain　the　final　soft　measuring　model.　Thus,　multiple　kernel　and　regularization　parameters　are　adaptive　selected　for　building　SEN-LSSVM　model.　UCI　benchmark　datasets　and　mechanical　frequency　spectral　data　are　used　to　validate　the　effectiveness　of　this　method.　(C)　2018,　IFAC　(International　Federation　of　Automatic　Control)　Hosting　by　Elsevier　Ltd.　All　rights　reserved.