{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002748939,0.00008239713,0.0001101116,0.0001174532,0.00003897388,0.00005394097,0.001888399,0.00005744544,0.00001566297],"category_scores_gemma":[0.00005111158,0.0000742479,0.00001878369,0.0005931758,0.00005580844,0.0006664359,0.001530625,0.00007207299,0.00005733954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001473458,"about_ca_system_score_gemma":0.00004393896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007052533,"about_ca_topic_score_gemma":0.0001808757,"domain_scores_codex":[0.9990055,0.00003668712,0.0001780816,0.0003947179,0.0002149599,0.0001700758],"domain_scores_gemma":[0.9977801,0.0001108048,0.00006969966,0.001956774,0.00004119401,0.00004144207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007868971,0.0001175035,0.0008032161,0.00002388331,0.00006060631,0.0000225857,0.0007002208,0.00005011419,0.04279694,0.1337412,0.1170611,0.7046148],"study_design_scores_gemma":[0.0007021895,0.000653866,0.03781919,0.00007898988,0.00002972453,0.00002935754,0.0005612673,0.2691111,0.6517939,0.02069072,0.01777534,0.000754301],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02282153,0.00000750665,0.9640991,0.0003453504,0.0002390352,0.0001365771,0.0001011596,0.002405335,0.00984442],"genre_scores_gemma":[0.9375783,0.00002563293,0.06170427,0.00005513275,0.00003265144,0.00000516611,0.0001319383,0.000008288841,0.0004586465],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9147567,"threshold_uncertainty_score":0.3509146,"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."}}