{"id":"W1534746498","doi":"","title":"Adaptive selection of ensembles for imbalanced class distributions","year":2012,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Skew; Computer science; Artificial intelligence; Boolean function; Class (philosophy); Reliability (semiconductor); Selection (genetic algorithm); Machine learning; Receiver operating characteristic; Pattern recognition (psychology); Data mining; Algorithm","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":[0.0003378514,0.0001356532,0.0001826642,0.0001044467,0.0001344822,0.00003929969,0.0004710646,0.00009396594,0.000009678002],"category_scores_gemma":[0.0001687653,0.0001332731,0.00007367754,0.0004769018,0.00006124897,0.0007742472,0.0001047522,0.0001014247,0.0000230747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230396,"about_ca_system_score_gemma":0.00006863748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001174114,"about_ca_topic_score_gemma":0.00001212613,"domain_scores_codex":[0.99889,0.00006297084,0.0002228926,0.0002688467,0.0001914218,0.0003638387],"domain_scores_gemma":[0.9987804,0.000168834,0.0002213412,0.0004975013,0.0002394245,0.00009249241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005610716,0.0002920996,0.005515925,0.00003666555,0.00004438053,2.368452e-7,0.0006868832,0.00002423724,0.09833217,0.8446615,0.03698014,0.01336964],"study_design_scores_gemma":[0.0006603259,0.0002995799,0.0477152,0.00005750915,0.0000291019,0.0000163313,0.0001469886,0.03145719,0.8102864,0.005233897,0.1036027,0.0004947716],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005021468,0.00006134908,0.9921232,0.001000794,0.0002344093,0.0003911163,0.000125214,0.0003415414,0.0007008702],"genre_scores_gemma":[0.8469905,0.00001421707,0.1523107,0.00005450485,0.00008599976,0.0001446129,0.00004778591,0.00001000982,0.000341612],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8419691,"threshold_uncertainty_score":0.5434721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02421894912144981,"score_gpt":0.2813993209002014,"score_spread":0.2571803717787515,"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."}}