{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006161717,0.001261418,0.001205363,0.003387833,0.0009284242,0.002498757,0.00151947,0.001233747,0.001669508],"category_scores_gemma":[0.01153048,0.0003567194,0.001318906,0.003013832,0.0006169025,0.002676947,0.001329624,0.001855033,0.0004762952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00193908,"about_ca_system_score_gemma":0.001143972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01049148,"about_ca_topic_score_gemma":0.005265753,"domain_scores_codex":[0.9974638,0.001216971,0.0001742857,0.000357245,0.0005318388,0.0002557209],"domain_scores_gemma":[0.9943071,0.003767882,0.0004333062,0.0003561504,0.0009703271,0.0001652646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006387964,0.0005772963,0.02668181,0.0001337586,0.0002928927,0.0003674683,0.0002610697,0.7784525,0.0005570667,0.01275725,0.002923366,0.1763567],"study_design_scores_gemma":[0.000003976815,0.00002459703,0.000871311,0.00001167369,0.00001366837,0.00002372379,0.0000213621,0.9961134,0.0001249919,0.00253186,0.0002516239,0.000007640069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5019006,0.006096859,0.4740087,0.00519892,0.0005903494,0.0002448726,0.0007238514,0.001161592,0.01007417],"genre_scores_gemma":[0.9603931,0.000906288,0.03506031,0.0001369518,0.0001399285,0.00007243482,0.00033522,0.0000264819,0.002929388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01049148,"threshold_uncertainty_score":0.03258669,"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."}}