{"id":"W1981838066","doi":"10.1109/icdmw.2012.122","title":"The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Concept drift; Computer science; Machine learning; Confusion matrix; Artificial intelligence; Data mining; Benchmarking; Class (philosophy); Algorithm; Data stream mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006582935,0.00007527322,0.00007400997,0.00003263955,0.0001090051,0.0001188246,0.001785,0.00003847384,0.000002848504],"category_scores_gemma":[0.00009732159,0.00005129246,0.0000148455,0.0001605999,0.0000388459,0.00105957,0.0006598546,0.00007362336,0.000008464967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002549588,"about_ca_system_score_gemma":0.00001941404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007265613,"about_ca_topic_score_gemma":0.00007138738,"domain_scores_codex":[0.9991826,0.00003623061,0.0001374557,0.0002393951,0.0001103916,0.0002938642],"domain_scores_gemma":[0.9983793,0.0002290715,0.00004444876,0.00128001,0.00002597291,0.00004115297],"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":[9.101192e-7,0.00001688446,0.0001323817,6.929839e-7,0.000002503479,1.573288e-7,0.000132045,1.320187e-7,0.0001529881,0.004059206,0.006361729,0.9891404],"study_design_scores_gemma":[0.0004781816,0.0001380811,0.003649454,0.00001411543,0.000005019921,0.00001631999,0.0001558275,0.7305166,0.05526706,0.002300428,0.2071634,0.0002955227],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003609848,0.00009393881,0.9977558,0.0002665423,0.0003363536,0.0002274343,0.00002736024,0.0002740849,0.0006575176],"genre_scores_gemma":[0.3071823,0.00001601692,0.692199,0.0001416767,0.0001319423,0.0000687324,0.00003490036,0.000007377129,0.0002180982],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9888449,"threshold_uncertainty_score":0.3317004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03414515557812448,"score_gpt":0.3070543088044025,"score_spread":0.2729091532262781,"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."}}