{"id":"W4213295491","doi":"10.1101/2022.02.21.481356","title":"Supervised Rank aggregation (SRA): A novel rank aggregation approach for ensemble-based feature selection","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada","keywords":"Feature selection; Categorical variable; Computer science; Feature (linguistics); Rank (graph theory); Artificial intelligence; Machine learning; Data mining; Ensemble learning; Pattern recognition (psychology); 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":[0.004558569,0.001396006,0.002270278,0.002456041,0.0007970143,0.001353663,0.001621067,0.0009517351,0.001695287],"category_scores_gemma":[0.007384159,0.0004410288,0.001871336,0.002224031,0.0004953574,0.001151985,0.001370885,0.001361526,0.0007624218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004441671,"about_ca_system_score_gemma":0.001299028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003067232,"about_ca_topic_score_gemma":0.003549052,"domain_scores_codex":[0.9967656,0.001284086,0.000248901,0.0005199442,0.0009886487,0.0001928287],"domain_scores_gemma":[0.9947357,0.002152631,0.0005225137,0.0007906884,0.001625178,0.0001734057],"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":[0.0002649773,0.0003153801,0.006565151,0.0002017039,0.0006597604,0.0001969794,0.0001876474,0.3042109,0.007715142,0.006552929,0.008503375,0.664626],"study_design_scores_gemma":[0.00001541057,0.0001005293,0.0008988641,0.00001213571,0.00005401384,0.00006540352,0.00001519311,0.9926334,0.001377836,0.003684733,0.001125211,0.00001729825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01181405,0.0003762587,0.9863753,0.0001208169,0.00004980471,0.0000601171,0.0001219568,0.0005891024,0.0004925577],"genre_scores_gemma":[0.3940671,0.0004336152,0.6013961,0.0002135412,0.0003427538,0.0003457934,0.001117288,0.000180931,0.001902882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004558569,"threshold_uncertainty_score":0.02410829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07560128630664867,"score_gpt":0.3463822704480924,"score_spread":0.2707809841414438,"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."}}