{"id":"W2964276935","doi":"10.1002/sam.11410","title":"Pruning variable selection ensembles","year":2019,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Benchmark (surveying); Sorting; Selection (genetic algorithm); Ensemble learning; Lasso (programming language); Context (archaeology); Stability (learning theory); Pruning; Artificial intelligence; Feature selection; Machine learning; Process (computing); Boosting (machine learning); Variable (mathematics); Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004800423,0.0001044468,0.0001949467,0.0002650905,0.0009809582,0.001926045,0.004284411,0.00002682645,0.0002029051],"category_scores_gemma":[0.0007627705,0.00006286784,0.00001640288,0.002156484,0.0002040318,0.004781599,0.002977573,0.000240382,0.00003650157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001685783,"about_ca_system_score_gemma":0.0002351292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009872089,"about_ca_topic_score_gemma":0.00002520906,"domain_scores_codex":[0.9978335,0.0001107583,0.0002959508,0.0007242448,0.0006877414,0.0003477368],"domain_scores_gemma":[0.9974572,0.0004298421,0.000177338,0.001614915,0.0001397215,0.0001809738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007677697,0.0002815764,0.09560838,0.00004860533,0.001045452,0.00005106231,0.001805094,0.001516874,0.02445184,0.1156135,0.0805766,0.6789242],"study_design_scores_gemma":[0.0001236157,0.00004654391,0.01278443,0.00002723725,0.0001931208,0.0001057625,0.0003629666,0.9802229,0.00006369477,0.00251341,0.003426161,0.000130175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02657344,0.00005099702,0.9719622,0.0005662876,0.0001797072,0.00004677657,0.0002602225,0.00002007398,0.0003403278],"genre_scores_gemma":[0.312186,0.00008819917,0.6870064,0.0002696796,0.00007859768,7.399319e-7,0.0002973986,0.000003636901,0.00006929717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.978706,"threshold_uncertainty_score":0.99911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04718228723840193,"score_gpt":0.3264302161271736,"score_spread":0.2792479288887716,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}