{"id":"W4388097990","doi":"10.5267/j.ijdns.2023.9.009","title":"Efficient credit card fraud detection using evolutionary hybrid feature selection and random weight networks","year":2023,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Credit card fraud; Computer science; Credit card; Feature selection; Machine learning; Artificial intelligence; Feature (linguistics); Cornerstone; Process (computing); Data mining; Payment","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001505673,0.0008162339,0.0009640923,0.001589056,0.0004736055,0.0008900041,0.0009726779,0.0006643083,0.0006570964],"category_scores_gemma":[0.00320201,0.0002964767,0.0005950726,0.001090399,0.0003206298,0.00103656,0.0006165057,0.0004839831,0.0002430481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007107293,"about_ca_system_score_gemma":0.0005826067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004501276,"about_ca_topic_score_gemma":0.003985517,"domain_scores_codex":[0.9994385,0.0001480224,0.00003888676,0.000129444,0.0001701032,0.00007510459],"domain_scores_gemma":[0.9990049,0.0004057451,0.0001552101,0.0001112476,0.0002778297,0.00004507825],"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":[0.0005342847,0.0004723109,0.01253991,0.00006503596,0.0001457561,0.0002271518,0.0001070372,0.3488779,0.01298619,0.00221952,0.002273266,0.6195517],"study_design_scores_gemma":[0.000008322363,0.00003714476,0.0006259343,0.000002862354,0.000009512798,0.00002468221,0.000007577902,0.9971426,0.001533974,0.0003945285,0.0002073409,0.000005399263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2596291,0.0004307054,0.7347427,0.0003979274,0.0001103975,0.0002165422,0.0001549681,0.001789072,0.002528576],"genre_scores_gemma":[0.862421,0.0001047648,0.1354277,0.0001116531,0.00003494179,0.000112424,0.0002268238,0.00003893038,0.001521805],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004501276,"threshold_uncertainty_score":0.008950174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124885327068184,"score_gpt":0.2923868420032908,"score_spread":0.271137988732609,"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."}}