{"id":"W2970232071","doi":"10.48550/arxiv.1908.11553","title":"Credit Card Fraud Detection Using Autoencoder Neural Network","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Oversampling; Autoencoder; Artificial intelligence; Artificial neural network; Computer science; Noise (video); Noise reduction; Class (philosophy); Credit card fraud; Pattern recognition (psychology); Sample (material); Machine learning; Credit card; Data mining; Bandwidth (computing); Telecommunications","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":[0.001159879,0.0005783306,0.0007232674,0.001212016,0.0003532363,0.0009059229,0.0006897459,0.0007895126,0.0005190025],"category_scores_gemma":[0.002468807,0.0002838517,0.0004440308,0.0008591993,0.0003413759,0.001219265,0.0006342959,0.001020971,0.0002455813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000735982,"about_ca_system_score_gemma":0.0004751667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004476365,"about_ca_topic_score_gemma":0.003080384,"domain_scores_codex":[0.9993469,0.0001443846,0.00004588504,0.0001567737,0.000221307,0.00008473228],"domain_scores_gemma":[0.9992011,0.0002634093,0.000124064,0.00009097324,0.0002889806,0.00003140547],"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":[0.0004178913,0.0003550078,0.01716522,0.00007775172,0.0001875959,0.0001841228,0.0001309181,0.280511,0.008912261,0.003776425,0.004521166,0.6837606],"study_design_scores_gemma":[0.000003246458,0.00001644012,0.001202439,0.000005190849,0.000008290705,0.00002369934,0.00001129061,0.9960436,0.001486467,0.0008498443,0.0003444457,0.000005132561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3435727,0.001924152,0.646221,0.0009761764,0.000298309,0.0001089009,0.0002303804,0.001235355,0.005432853],"genre_scores_gemma":[0.9368217,0.0005011033,0.05954258,0.0001480798,0.00009382771,0.00003486087,0.0002856454,0.00001767485,0.002554476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004476365,"threshold_uncertainty_score":0.008900642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09316145390117792,"score_gpt":0.2064884997316957,"score_spread":0.1133270458305178,"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."}}