{"id":"W4403839440","doi":"10.1080/10618600.2024.2421248","title":"Multi-label Random Subspace Ensemble Classification","year":2024,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; National Institute on Aging; National Institutes of Health; National Science Foundation","keywords":"Random forest; Random subspace method; Computer science; Classifier (UML); Subspace topology; Artificial intelligence; Ensemble learning; Linear subspace; Machine learning; Pattern recognition (psychology); Multinomial distribution; Multi-label classification; Data mining; Algorithm; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.003272942,0.001082008,0.002353039,0.001775506,0.0007871704,0.001315618,0.002276212,0.00142429,0.002293906],"category_scores_gemma":[0.005851578,0.0003175094,0.001415071,0.001561605,0.0005364108,0.002838686,0.001548226,0.001830986,0.001569554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000605804,"about_ca_system_score_gemma":0.0008577617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002707035,"about_ca_topic_score_gemma":0.003887202,"domain_scores_codex":[0.9973094,0.001064103,0.00009469668,0.0005127472,0.0007966267,0.0002222981],"domain_scores_gemma":[0.9968057,0.001175411,0.0002004913,0.0006986216,0.0009754056,0.0001443686],"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":[0.0002128326,0.0002689729,0.004104063,0.0001157009,0.0002222252,0.00009952512,0.0001222523,0.3254472,0.002816277,0.01349582,0.01187699,0.6412181],"study_design_scores_gemma":[0.000005633073,0.0000259091,0.0001991264,0.000008077221,0.00001168221,0.00003065847,0.00001406794,0.9915706,0.000895559,0.005731257,0.001496426,0.00001095117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01129061,0.000721577,0.9850879,0.0001758834,0.0001148988,0.00005868936,0.0001703796,0.0009469531,0.001433052],"genre_scores_gemma":[0.448824,0.0006905093,0.5410928,0.0004778955,0.0004441216,0.0002875842,0.002230722,0.000274412,0.005677873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003272942,"threshold_uncertainty_score":0.01730913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03378278262940175,"score_gpt":0.3029536130098509,"score_spread":0.2691708303804491,"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."}}