{"id":"W4360765013","doi":"10.1109/asonam55673.2022.10068715","title":"Dynamic Ensemble Associative Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Associative property; Artificial intelligence; Random forest; Machine learning; Ensemble learning; Random subspace method; Feature (linguistics); Feature vector; Content-addressable memory; Process (computing); Feature selection; Pattern recognition (psychology); Data mining; Support vector machine; Artificial neural network; 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":[0.0001931193,0.00003701485,0.00004499143,0.00002942928,0.0004317859,0.0000609235,0.000470968,0.000006551282,0.00009953521],"category_scores_gemma":[0.00001488216,0.00003777486,0.00001939611,0.0002869906,0.000005710189,0.0001312013,0.0005123555,0.0001403836,0.00007268797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006210811,"about_ca_system_score_gemma":0.00002728282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002896303,"about_ca_topic_score_gemma":0.000003360057,"domain_scores_codex":[0.9994614,0.00004000582,0.00006259889,0.0001736004,0.0001476076,0.0001147894],"domain_scores_gemma":[0.9996868,0.00005519459,0.00003776744,0.0001804562,0.00001530486,0.00002441306],"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":[7.026957e-7,0.0001280905,0.0005148549,0.000001509121,0.00002538496,0.000008005837,0.00256147,0.001647694,0.00114433,0.3177348,0.0152596,0.6609735],"study_design_scores_gemma":[0.0000795529,0.00004828603,0.001091984,4.664192e-7,0.000001331369,0.000005935115,0.0004076323,0.9040046,0.00006611568,0.002891398,0.09131414,0.00008850243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004271903,0.00001952991,0.9671212,0.001586577,0.00009200817,0.00005099731,0.000005648007,0.0002491567,0.02660297],"genre_scores_gemma":[0.6718398,0.000003957901,0.3008413,0.0005064387,0.00001061376,0.0001122149,0.00002730384,0.000006839,0.02665153],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.902357,"threshold_uncertainty_score":0.3320992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009357205294370665,"score_gpt":0.2471810620330675,"score_spread":0.2378238567386968,"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."}}