{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003497677,0.001310861,0.002139472,0.0019017,0.0008811745,0.001024086,0.001485155,0.0009149514,0.001532992],"category_scores_gemma":[0.009722313,0.0004254415,0.001048752,0.001623639,0.0005038775,0.001196348,0.002078332,0.00115235,0.0006498214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003952122,"about_ca_system_score_gemma":0.0009934779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001426937,"about_ca_topic_score_gemma":0.002450441,"domain_scores_codex":[0.9973104,0.0009123812,0.000153398,0.0004540631,0.0009594172,0.0002103704],"domain_scores_gemma":[0.9962562,0.001999042,0.0002448648,0.0005240164,0.0008645892,0.0001112509],"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":[0.000238992,0.000132033,0.008527879,0.0002116324,0.0003922064,0.0003213251,0.0001928781,0.3939775,0.01116501,0.01524609,0.007589722,0.5620047],"study_design_scores_gemma":[0.00001738368,0.00009861111,0.001125364,0.00003531486,0.00007859714,0.0001386989,0.00003924466,0.9809245,0.003909572,0.009699439,0.003918064,0.00001536484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03536737,0.001143799,0.9601498,0.0001497635,0.0001178747,0.00009099136,0.0001464679,0.0005589525,0.00227496],"genre_scores_gemma":[0.5816959,0.001271878,0.409479,0.0004239248,0.0003097805,0.0005048491,0.001787316,0.0002219059,0.004305438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003497677,"threshold_uncertainty_score":0.01849771,"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."}}