{"id":"W4372324482","doi":"10.54691/bcpbm.v44i.4839","title":"Multiple Machine Learning Models on Credit Card Fraud Detection","year":2023,"lang":"en","type":"article","venue":"BCP Business & Management","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Confusion matrix; Computer science; Decision tree; Support vector machine; Machine learning; Categorical variable; Artificial intelligence; Credit card; F1 score; Receiver operating characteristic; Logistic regression; Set (abstract data type); Database transaction; Data mining; Data set; Test set; Tree (set theory); Database; 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":[0.0003476458,0.0001766239,0.0001465747,0.000434002,0.0002263377,0.0001389886,0.0007905711,0.00005495636,0.000006320297],"category_scores_gemma":[0.00005169048,0.0001763625,0.00004189255,0.001600729,0.00002309062,0.0006028285,0.0005532139,0.0001534078,0.0003638927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001027431,"about_ca_system_score_gemma":0.000009868587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009891346,"about_ca_topic_score_gemma":0.0000158703,"domain_scores_codex":[0.9984118,0.00005718358,0.0002250425,0.000548816,0.0004487969,0.0003084072],"domain_scores_gemma":[0.998827,0.00005091754,0.0001143293,0.0008464922,0.0001143818,0.00004683535],"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":[0.0000125782,0.00005224775,0.00004668639,0.00006774423,0.00002451252,0.00003139859,0.00007965734,0.04029295,0.0008037121,0.00556426,0.00295944,0.9500648],"study_design_scores_gemma":[0.0003598979,0.00001080441,0.0906103,0.00005098596,0.00001061508,0.000002048823,0.00002397609,0.8396419,0.003089335,0.004735082,0.06117785,0.0002871775],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006180912,0.00002466584,0.9868193,0.001047374,0.0005887371,0.0004661396,0.000009935231,0.003129299,0.001733673],"genre_scores_gemma":[0.9761826,0.0002062359,0.02106494,0.0001690098,0.0000910348,0.0002435929,0.0001126029,0.00002842442,0.001901492],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9700018,"threshold_uncertainty_score":0.7191854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04229496765723449,"score_gpt":0.2473526170253232,"score_spread":0.2050576493680887,"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."}}