{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004215906,0.001008924,0.002436918,0.002372977,0.0007969034,0.0017218,0.001444357,0.001469826,0.001598486],"category_scores_gemma":[0.008393167,0.0009535576,0.001853585,0.001428486,0.0004722411,0.003079328,0.001989238,0.001824795,0.0006100821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005636225,"about_ca_system_score_gemma":0.0008849668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002167887,"about_ca_topic_score_gemma":0.002078706,"domain_scores_codex":[0.9987503,0.0003661976,0.0001190527,0.0002219297,0.0004127216,0.0001297985],"domain_scores_gemma":[0.9960387,0.002412267,0.0001869263,0.0002694423,0.0009711265,0.0001215551],"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":[0.0001663588,0.0001627428,0.002318676,0.0001261271,0.0003730849,0.00008158911,0.0001416496,0.4742007,0.003936905,0.01489254,0.002160684,0.501439],"study_design_scores_gemma":[0.000004754332,0.00001642185,0.0001059477,0.000008845448,0.00002394477,0.000008767503,0.00001001579,0.9910068,0.0006794811,0.007923014,0.0002061112,0.000005937613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01216529,0.0001521295,0.9868521,0.00005271378,0.00003130076,0.00002745642,0.00002771367,0.0001810469,0.0005102003],"genre_scores_gemma":[0.3733021,0.0003007686,0.624077,0.0001150579,0.00009273842,0.000254669,0.0002825952,0.0001024859,0.001472553],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004215906,"threshold_uncertainty_score":0.02229613,"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."}}