{"id":"W1995911512","doi":"10.1007/s00500-007-0227-2","title":"Building ensemble classifiers using belief functions and OWA operators","year":2007,"lang":"en","type":"article","venue":"Soft Computing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Cascading classifiers; Dempster–Shafer theory; Classifier (UML); Random subspace method; Computer science; Machine learning; Operator (biology); Flexibility (engineering); Combing; Process (computing); Ensemble learning; Pattern recognition (psychology); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007484376,0.0002415753,0.0003773219,0.0006784978,0.001099618,0.0008659522,0.0005258762,0.0001376973,0.00009365374],"category_scores_gemma":[0.004248625,0.0002085736,0.0001135415,0.001323187,0.0001439251,0.000413065,0.0006355525,0.0003159668,0.00008217457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001084153,"about_ca_system_score_gemma":0.00008069085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004563233,"about_ca_topic_score_gemma":0.00002339653,"domain_scores_codex":[0.9960843,0.0001774546,0.001052375,0.000841706,0.001195348,0.0006488213],"domain_scores_gemma":[0.9949499,0.00350941,0.0003221722,0.0005555213,0.00038601,0.0002770207],"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":[0.00004130236,0.00005113456,0.04808858,0.000008620317,0.00003046377,0.0000699509,0.00156881,0.01015482,0.0764677,0.002764018,0.001420284,0.8593343],"study_design_scores_gemma":[0.001429107,0.00009288381,0.03672855,0.0002632214,0.00004427835,0.0003251304,0.008495811,0.9040795,0.003819152,0.009997958,0.03368685,0.001037564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5187824,0.0001137838,0.4789748,0.00005208181,0.001000372,0.00008605486,0.000001485886,0.00007261612,0.000916331],"genre_scores_gemma":[0.8763955,8.041581e-7,0.1227224,0.0002874035,0.0003581076,4.032067e-7,5.789166e-7,0.00002642173,0.0002083602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8939247,"threshold_uncertainty_score":0.8505386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1728347046635952,"score_gpt":0.4355230489208196,"score_spread":0.2626883442572243,"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."}}