{"id":"W2789731619","doi":"10.1016/j.eswa.2018.03.021","title":"Adapting dynamic classifier selection for concept drift","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Concept drift; Computer science; Classifier (UML); Artificial intelligence; Machine learning; A priori and a posteriori; Data mining","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":[0.008375608,0.001395238,0.002782407,0.003033641,0.001145794,0.002545311,0.003930773,0.00251777,0.00242106],"category_scores_gemma":[0.02272746,0.0006927662,0.001329235,0.002579462,0.0006170745,0.003199107,0.00244916,0.00287149,0.001721167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094838,"about_ca_system_score_gemma":0.002510848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003243024,"about_ca_topic_score_gemma":0.003559235,"domain_scores_codex":[0.9963273,0.0008399527,0.0002860606,0.001110258,0.001150284,0.0002861662],"domain_scores_gemma":[0.9886798,0.005913348,0.0004217226,0.001401398,0.003207877,0.0003758918],"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":[0.0006725167,0.0007529039,0.009381339,0.0001780393,0.0004491344,0.0003203857,0.0002130977,0.1077642,0.01350598,0.003855002,0.01438634,0.8485209],"study_design_scores_gemma":[0.00004111945,0.0001017009,0.000792411,0.00001536196,0.00007271854,0.0001569906,0.00003772206,0.9898959,0.002673818,0.004293418,0.001903008,0.00001570682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07524928,0.00181227,0.9161742,0.0008310106,0.0007437671,0.0002989649,0.0003516283,0.002852138,0.001686776],"genre_scores_gemma":[0.6458833,0.0007026991,0.3443685,0.0007922705,0.0007803733,0.0004226434,0.001790221,0.0005438485,0.004716122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008375608,"threshold_uncertainty_score":0.04429501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0181693000240935,"score_gpt":0.2960006961471375,"score_spread":0.277831396123044,"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."}}