{"id":"W4413912380","doi":"10.5267/j.ijdns.2025.7.003","title":"EFC-Tomek: An effective undersampling technique for credit card fraud detection","year":2025,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Undersampling; Credit card fraud; Credit card; Computer science; Business; Artificial intelligence; Finance; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002619565,0.00009091148,0.0001363356,0.000368251,0.0002216467,0.0004401981,0.003843592,0.00004817311,7.812326e-7],"category_scores_gemma":[0.000360755,0.00008094948,0.00002855858,0.0006003922,0.0002041752,0.003647976,0.0007175684,0.0001665512,3.864631e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001320224,"about_ca_system_score_gemma":0.0002054214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009232602,"about_ca_topic_score_gemma":0.00001020514,"domain_scores_codex":[0.9986302,0.00004751637,0.000318862,0.0003770376,0.0004421627,0.0001842535],"domain_scores_gemma":[0.9980549,0.0002982522,0.0002967889,0.0005483321,0.000726927,0.00007476272],"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":[0.0001319952,0.0001051596,0.001225673,0.00001953619,0.00009748309,0.000009239517,0.0001704744,0.000523296,0.09612465,0.06393467,0.004315601,0.8333423],"study_design_scores_gemma":[0.001563487,0.0009770247,0.02170152,0.0007431306,0.00006573364,0.00043271,0.0001591563,0.4636634,0.2576783,0.1868153,0.06557188,0.0006283354],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001621579,0.0001041891,0.9958696,0.0006318694,0.001316039,0.000251054,0.00004171001,0.00004283139,0.0001210801],"genre_scores_gemma":[0.711823,0.00008838069,0.2874409,0.0002540826,0.0003459618,0.00002006028,0.00001544672,0.000003428744,0.000008683315],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8327139,"threshold_uncertainty_score":0.7142413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03184383700583517,"score_gpt":0.3644750339257956,"score_spread":0.3326311969199604,"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."}}