{"id":"W1495850371","doi":"10.1109/iconip.2002.1198971","title":"Learning aggregation for combining classifier ensembles","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Classifier (UML); Computer science; Random subspace method; Artificial intelligence; Machine learning; Training set; Cascading classifiers; Ensemble learning; Architecture; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.009205477,0.00161945,0.002557672,0.002850835,0.001172285,0.002132338,0.001732631,0.001366255,0.001818687],"category_scores_gemma":[0.01668752,0.0008646605,0.001819667,0.002806063,0.0008080531,0.00352645,0.003428638,0.002224504,0.001046551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000860116,"about_ca_system_score_gemma":0.001003767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001970146,"about_ca_topic_score_gemma":0.002915107,"domain_scores_codex":[0.9953741,0.001590501,0.000421031,0.0008194527,0.001484233,0.0003105826],"domain_scores_gemma":[0.9922243,0.003252092,0.0005892375,0.001974212,0.001738447,0.0002216809],"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":[0.0001531273,0.0001581936,0.003573226,0.0002391577,0.0005525697,0.0001316815,0.0003488976,0.2808162,0.00756803,0.01627344,0.003769381,0.6864161],"study_design_scores_gemma":[0.0000200266,0.0001820141,0.00103603,0.00004916299,0.0001637957,0.000121506,0.00007340068,0.9514749,0.00652611,0.03429022,0.006024205,0.00003860261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01266277,0.0004627351,0.9836714,0.0001225687,0.00009147215,0.0001334447,0.0000542555,0.001198859,0.001602529],"genre_scores_gemma":[0.2688484,0.000567273,0.7269333,0.0001751342,0.0002717467,0.0004508346,0.0004768219,0.0001633215,0.002113206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009205477,"threshold_uncertainty_score":0.04868376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02749632459450716,"score_gpt":0.2602540664509531,"score_spread":0.2327577418564459,"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."}}