{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002900452,0.001105997,0.00170808,0.002081741,0.0007501277,0.001336135,0.002065504,0.001033662,0.003305711],"category_scores_gemma":[0.006480927,0.000379337,0.0009853636,0.002300702,0.0006115655,0.002896153,0.001928294,0.001337873,0.001260846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006280767,"about_ca_system_score_gemma":0.0008847155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002584733,"about_ca_topic_score_gemma":0.004915963,"domain_scores_codex":[0.9980331,0.0005116348,0.0001280443,0.0004680683,0.0007180966,0.0001410053],"domain_scores_gemma":[0.9958972,0.001847181,0.0002419122,0.0007627852,0.001130239,0.0001206645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001393238,0.0003530724,0.006871495,0.0001688238,0.0003583945,0.0001275153,0.0001557612,0.1578402,0.002230954,0.01318312,0.006236656,0.8123347],"study_design_scores_gemma":[0.00001614115,0.0001597748,0.00124053,0.00003659094,0.00009201602,0.0001693582,0.00006013463,0.9688902,0.002937329,0.02045449,0.005915137,0.0000281951],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07451881,0.00262304,0.906154,0.0003973959,0.0003269112,0.0002620558,0.0004150378,0.002051077,0.01325166],"genre_scores_gemma":[0.7501236,0.001914564,0.2350365,0.0005466997,0.0003503683,0.0005651626,0.001268634,0.0001565756,0.01003782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003305711,"threshold_uncertainty_score":0.01533926,"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."}}