{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002815991,0.0003438989,0.0003558913,0.0002511382,0.0002088971,0.0002130683,0.002225539,0.0004438245,0.00001428057],"category_scores_gemma":[0.00003015703,0.0004229315,0.0001978086,0.0007372316,0.00008983615,0.0007767128,0.002275907,0.0008091544,0.00008532689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000468437,"about_ca_system_score_gemma":0.000207054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002384675,"about_ca_topic_score_gemma":0.00002842182,"domain_scores_codex":[0.9976364,0.00019541,0.0002514293,0.00131023,0.0001443298,0.0004621664],"domain_scores_gemma":[0.9969351,0.0000778696,0.0004210222,0.00223022,0.0002131534,0.0001226354],"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":[0.00001240701,0.00002477039,0.001789876,0.00004240853,0.00004389835,0.0000498369,0.00005175035,0.9810923,0.0002130081,0.01467301,0.0008866573,0.001120124],"study_design_scores_gemma":[0.0001508552,0.00003122567,0.001968685,0.0000578961,0.00004143678,0.000008053751,0.00001010713,0.9835032,0.0005225466,0.01179061,0.001484835,0.0004305608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05744242,0.00004066931,0.9380277,0.00004655392,0.00221141,0.0003948909,0.00002410757,0.0009526426,0.0008596111],"genre_scores_gemma":[0.9762773,0.00005227629,0.02264901,0.0001283099,0.0003079174,0.00000155284,0.00003436317,0.00002715266,0.0005221501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9188349,"threshold_uncertainty_score":0.9998223,"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."}}