{"id":"W2016375676","doi":"10.1016/j.patcog.2014.12.003","title":"META-DES: A dynamic ensemble selection framework using meta-learning","year":2014,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":243,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Classifier (UML); Computer science; Artificial intelligence; Margin classifier; Machine learning; Pattern recognition (psychology); Ensemble learning; Quadratic classifier; Random subspace method","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":[0.002105982,0.001205692,0.001871022,0.001501811,0.0006853829,0.001259149,0.002580903,0.001335944,0.003243721],"category_scores_gemma":[0.002713419,0.0005832654,0.001426568,0.001368867,0.0003927499,0.00185068,0.001969122,0.001325518,0.001068794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005302699,"about_ca_system_score_gemma":0.000816461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002254606,"about_ca_topic_score_gemma":0.004973335,"domain_scores_codex":[0.9990414,0.0003200101,0.00004698627,0.0001884304,0.0003178609,0.00008529058],"domain_scores_gemma":[0.9988711,0.0004678367,0.00006050669,0.0002084828,0.0003203271,0.00007175493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002668708,0.0002304245,0.0016793,0.0001083567,0.0006148746,0.0001242007,0.00008665435,0.5242172,0.005610622,0.01032441,0.006376082,0.450361],"study_design_scores_gemma":[0.00001263898,0.00003825948,0.0001177956,0.000006285515,0.00003275489,0.00003104965,0.000008804651,0.9924524,0.001097908,0.004703404,0.00149045,0.00000813406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007479871,0.0004202032,0.9895177,0.0001040496,0.00007804517,0.00004351555,0.0001126748,0.001036162,0.001207847],"genre_scores_gemma":[0.3162037,0.0005059171,0.6744791,0.0003739898,0.0002491916,0.0002845121,0.0009469794,0.0004629388,0.00649376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003243721,"threshold_uncertainty_score":0.0111376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09331796022211099,"score_gpt":0.2706672145224036,"score_spread":0.1773492543002926,"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."}}