{"id":"W2578816095","doi":"10.1109/ictai.2016.0153","title":"Handling Concept Drifts Using Dynamic Selection of Classifiers","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Selection (genetic algorithm); Machine learning; Concept drift; Artificial intelligence; Rank (graph theory); Random subspace method; Data mining; Range (aeronautics); Support vector machine; Engineering; 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":[0.01026345,0.001405957,0.002330662,0.002382827,0.001061837,0.00230915,0.003236983,0.001365335,0.001779521],"category_scores_gemma":[0.01848549,0.0007224297,0.001025263,0.001717349,0.0007701577,0.004097389,0.002834916,0.002511709,0.001107406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007966396,"about_ca_system_score_gemma":0.001925533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002465499,"about_ca_topic_score_gemma":0.003252065,"domain_scores_codex":[0.9961762,0.0009289376,0.0002758353,0.001032747,0.001291623,0.0002944686],"domain_scores_gemma":[0.9904108,0.004604085,0.0008103297,0.00147763,0.002318546,0.0003786188],"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":[0.0005613443,0.000513471,0.0125399,0.0002228383,0.0004012715,0.0003582449,0.000417928,0.1127027,0.01195654,0.00640122,0.01021377,0.8437107],"study_design_scores_gemma":[0.00006082633,0.0001909343,0.001802379,0.00003143394,0.0000805671,0.0003134448,0.0001054044,0.9713237,0.007465383,0.01096417,0.007616151,0.00004564056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04060066,0.001396327,0.9529899,0.0003734218,0.0002556657,0.0003329003,0.000231142,0.002407442,0.00141243],"genre_scores_gemma":[0.5662093,0.0006875926,0.4260566,0.0006295214,0.0005563067,0.0005601774,0.001123312,0.0003696375,0.003807546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01026345,"threshold_uncertainty_score":0.05427891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02178333422573239,"score_gpt":0.2814879842778596,"score_spread":0.2597046500521272,"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."}}