{"id":"W4360977633","doi":"10.59200/iconic.2022.032","title":"Improving Accuracy of Credit Card Fraud Detection Using Supervised Machine Learning Models and Dimension Reduction","year":2022,"lang":"en","type":"article","venue":"International Conference on Intelligent and Innovative Computing Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Credit card; Credit card fraud; Dimensionality reduction; Dimension (graph theory); Machine learning; Computer science; Artificial intelligence; Identity theft; Reduction (mathematics); Computer security; Mathematics; Payment","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":[0.003700573,0.0008143519,0.001167269,0.001890721,0.0004897002,0.001716683,0.000692311,0.0009428886,0.0006860805],"category_scores_gemma":[0.01318901,0.0003116514,0.001045452,0.001020724,0.0003385149,0.001388048,0.0006022792,0.001057566,0.0005100833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008207083,"about_ca_system_score_gemma":0.001029365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007361866,"about_ca_topic_score_gemma":0.004476456,"domain_scores_codex":[0.9972699,0.001045519,0.0003162601,0.0003457619,0.0007659276,0.0002565181],"domain_scores_gemma":[0.9913861,0.003665731,0.0007184298,0.001682871,0.002403309,0.0001436199],"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":[0.0009870519,0.001099536,0.07336865,0.0001966737,0.0003948974,0.0002586763,0.0002855565,0.4975998,0.01313647,0.002251862,0.006136216,0.4042846],"study_design_scores_gemma":[0.000008984121,0.00007844708,0.004645425,0.00001691264,0.00002065478,0.00004888089,0.00004348817,0.9890084,0.004976412,0.0007022276,0.0004328598,0.00001724171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8106563,0.001041814,0.1787279,0.0009796324,0.0003010884,0.0001112663,0.0006133032,0.003344713,0.004224],"genre_scores_gemma":[0.9487611,0.0001560894,0.04981742,0.0000618545,0.00002932682,0.00002366193,0.0005051977,0.00002701236,0.0006183223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007361866,"threshold_uncertainty_score":0.01957071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09455430942438646,"score_gpt":0.324030225265639,"score_spread":0.2294759158412525,"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."}}