{"id":"W2146492981","doi":"10.6000/1929-6029.2012.01.02.11","title":"Feature Selection in Statistical Classification","year":2012,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wellcome Trust","keywords":"Feature selection; Pattern recognition (psychology); Artificial intelligence; Selection (genetic algorithm); Feature (linguistics); Computer science; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004325696,0.001074569,0.001841969,0.002521638,0.0005165558,0.001590893,0.001097692,0.001259534,0.002653135],"category_scores_gemma":[0.01053261,0.0004300683,0.001351919,0.0043984,0.0008572318,0.00167264,0.0008598198,0.001534222,0.001982917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005226912,"about_ca_system_score_gemma":0.0006298348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001227092,"about_ca_topic_score_gemma":0.0007615243,"domain_scores_codex":[0.9966846,0.001447726,0.0002733535,0.0006081739,0.0008614803,0.0001246331],"domain_scores_gemma":[0.9950008,0.003712409,0.0002618212,0.0004724779,0.0004886244,0.00006400071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001426666,0.0001002692,0.002756445,0.00128527,0.0003374636,0.0002679804,0.0001446553,0.0453743,0.006411751,0.05903625,0.0194046,0.8647383],"study_design_scores_gemma":[0.00007446093,0.0003820734,0.006702977,0.0004724094,0.000259393,0.0008611262,0.00008663863,0.5526195,0.0137354,0.3581239,0.06652805,0.000154039],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002639849,0.007654033,0.9874098,0.0004346803,0.0002107717,0.00004667049,0.0002608172,0.0005020863,0.000841255],"genre_scores_gemma":[0.1471109,0.01857765,0.8236025,0.0007327601,0.002368202,0.0007485277,0.001868358,0.000400731,0.004590361],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004325696,"threshold_uncertainty_score":0.0228768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2702167256840201,"score_gpt":0.5786589139916529,"score_spread":0.3084421883076328,"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."}}