{"id":"W4390481894","doi":"10.1109/ssci52147.2023.10371848","title":"Detection of Real Concept Drift Under Noisy Data Stream","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Concept drift; Computer science; Data stream; Bayesian probability; Data stream mining; Artificial intelligence; Classifier (UML); Surprise; Entropy (arrow of time); Data mining; Noise (video); Streaming data; Pattern recognition (psychology)","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":[0.008358525,0.001132766,0.001881305,0.002972655,0.0007458292,0.002831209,0.00168426,0.001248267,0.0003428718],"category_scores_gemma":[0.03348908,0.0003928944,0.0007430685,0.001857168,0.0009350601,0.003829708,0.00180215,0.002171252,0.0003479603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055168,"about_ca_system_score_gemma":0.001451116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001687131,"about_ca_topic_score_gemma":0.00120511,"domain_scores_codex":[0.9962372,0.0006235662,0.0003335692,0.0008824416,0.001730413,0.0001927669],"domain_scores_gemma":[0.9819723,0.009471846,0.002885446,0.00152879,0.00359792,0.0005437725],"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":[0.001018833,0.0004597624,0.09182826,0.0007609744,0.0005738719,0.0009869046,0.001172844,0.1705331,0.02301359,0.01031679,0.006958209,0.6923769],"study_design_scores_gemma":[0.00001969696,0.0001695003,0.008353307,0.00006505638,0.0000506449,0.0005211909,0.0002115954,0.9608113,0.01376756,0.01310437,0.002879315,0.00004655698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1693528,0.0025938,0.8238351,0.0006424283,0.0002991903,0.0001986021,0.0004886,0.001494479,0.001095017],"genre_scores_gemma":[0.7516531,0.000875415,0.244928,0.0003004049,0.0002321628,0.0001477106,0.0009175522,0.000139411,0.0008062951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008358525,"threshold_uncertainty_score":0.04420465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05751812953247639,"score_gpt":0.3162852276134214,"score_spread":0.2587670980809451,"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."}}