{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001268231,0.00006826869,0.00009506373,0.00009125515,0.00003987436,0.000029319,0.0004069618,0.00004419134,0.00002244138],"category_scores_gemma":[0.0000417166,0.00004680706,0.00002677434,0.0002073439,0.00005764427,0.0004850717,0.0001364603,0.00003297769,0.000004079504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006261164,"about_ca_system_score_gemma":0.00005426952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004744677,"about_ca_topic_score_gemma":0.00001206322,"domain_scores_codex":[0.9993417,0.00002732479,0.0001496688,0.0002119657,0.0001249401,0.0001444435],"domain_scores_gemma":[0.9994755,0.00006323281,0.00008463781,0.0002855821,0.00005861853,0.00003241706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004919017,0.00003730935,0.002733413,0.000007995219,0.00002313702,0.000002484153,0.0002484555,0.00001217449,0.6493058,0.03909519,0.001703932,0.3068252],"study_design_scores_gemma":[0.0003874037,0.0001778462,0.001256887,0.0001875958,0.000009101369,0.0000283782,0.0000278534,0.2934144,0.6985101,0.004918343,0.0008078563,0.0002742515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05827014,0.000006813294,0.9402426,0.000124806,0.00009570605,0.00005032453,0.000002556973,0.0003098419,0.0008972383],"genre_scores_gemma":[0.6806759,0.000002578136,0.3191395,0.00002102977,0.000006566088,0.000001164931,4.326124e-7,0.00000357359,0.0001493057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6224058,"threshold_uncertainty_score":0.1908737,"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."}}