{"id":"W4385819907","doi":"10.1109/access.2023.3304912","title":"Dynamic Ensemble Algorithm Post-Selection Using Hardness-Aware Oracle","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Universidade de Pernambuco; Universidade Federal de Pernambuco; Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; École de technologie supérieure; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Oracle; Computer science; Machine learning; Classifier (UML); Naive Bayes classifier; Artificial intelligence; Selection (genetic algorithm); Ensemble learning; Homogeneous; Algorithm; Perceptron; Data mining; Support vector machine; Artificial neural network; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002679703,0.0001630107,0.0001640023,0.0003345842,0.0002155783,0.0006497626,0.001785808,0.00008874171,0.00001200193],"category_scores_gemma":[0.0000345613,0.0001696731,0.00005047307,0.001605624,0.00003097634,0.002287289,0.0005618226,0.0001485381,0.0001431521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001119205,"about_ca_system_score_gemma":0.0001127993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005304637,"about_ca_topic_score_gemma":0.00006682189,"domain_scores_codex":[0.9985312,0.00005497851,0.0002066272,0.0005090627,0.0003043665,0.0003938159],"domain_scores_gemma":[0.9989532,0.00005199536,0.0001086369,0.0006355077,0.0001753345,0.00007536325],"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":[0.000007780519,0.00009792396,0.001364303,0.00005316039,0.00004294608,0.0001677875,0.0004061904,0.0004267087,0.02913655,0.0003863418,0.01443185,0.9534785],"study_design_scores_gemma":[0.0001396611,0.00006365829,0.003893279,0.00005265713,0.000009442379,0.00006098272,0.00001899325,0.9570251,0.03457021,0.00268046,0.001176605,0.00030892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09968871,0.00001241063,0.896489,0.0002115999,0.001028204,0.0001584112,0.00003415491,0.002248408,0.0001291115],"genre_scores_gemma":[0.7598282,0.00002593597,0.2391524,0.0002967931,0.0001121044,0.00004683932,0.00006571035,0.00004859789,0.0004233484],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9565984,"threshold_uncertainty_score":0.6919069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04848879585098689,"score_gpt":0.3576918152280549,"score_spread":0.3092030193770681,"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."}}