{"id":"W4391308145","doi":"10.1109/smc53992.2023.10394192","title":"A Post-Selection Algorithm for Improving Dynamic Ensemble Selection Methods","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Selection (genetic algorithm); Metric (unit); Classifier (UML); Data mining; Source code; Code (set theory); Selection algorithm; Artificial intelligence; Machine learning; Algorithm; Programming language; Set (abstract data type)","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":[0.005246299,0.002071411,0.002339209,0.002851782,0.00143585,0.001373414,0.003084836,0.001583164,0.006225763],"category_scores_gemma":[0.01264265,0.0007064648,0.001822686,0.002599789,0.0006452104,0.002446948,0.002155551,0.00253358,0.002715205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000831752,"about_ca_system_score_gemma":0.00190225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003965164,"about_ca_topic_score_gemma":0.005945128,"domain_scores_codex":[0.9955779,0.001169482,0.0002868178,0.0007759138,0.001869478,0.000320307],"domain_scores_gemma":[0.9926664,0.003048094,0.0002927209,0.001184913,0.00259777,0.000209994],"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":[0.0002795573,0.0002443521,0.002538143,0.0001102382,0.0002919056,0.0001398272,0.0001569331,0.1792376,0.007719939,0.008170321,0.01152026,0.789591],"study_design_scores_gemma":[0.00002600798,0.0001060908,0.0004021779,0.0000113796,0.00004029644,0.00005900308,0.00001994086,0.9881036,0.003286786,0.004144641,0.003785503,0.00001461899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005248907,0.0002382239,0.991585,0.00008703167,0.00009594048,0.0001106995,0.00008936906,0.001649022,0.0008958449],"genre_scores_gemma":[0.1378524,0.0003157893,0.8534718,0.0002929037,0.0003490405,0.0005974862,0.001523569,0.0006403806,0.004956683],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006225763,"threshold_uncertainty_score":0.02774543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0194271745282217,"score_gpt":0.3451315165545818,"score_spread":0.3257043420263601,"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."}}