{"id":"W4213324003","doi":"10.5220/0010812700003116","title":"Incremental Feature Learning for Fraud Data Stream","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 14th International Conference on Agents and Artificial Intelligence","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Feature (linguistics); Data stream; Artificial intelligence; Data mining","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":[],"consensus_categories":[],"category_scores_codex":[0.000450128,0.0001126208,0.0001055535,0.00009150342,0.0003611885,0.0002222241,0.003078237,0.00002826104,0.00008402535],"category_scores_gemma":[0.0002114855,0.00009313002,0.0000352117,0.000179958,0.0000818349,0.0004589425,0.001851417,0.0002578401,0.00000238707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000505202,"about_ca_system_score_gemma":0.00003861597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001440201,"about_ca_topic_score_gemma":0.0000016153,"domain_scores_codex":[0.9986945,0.00001352519,0.0002484278,0.0004330732,0.0004655824,0.0001449002],"domain_scores_gemma":[0.9991263,0.0000443127,0.0002968566,0.0002480061,0.0002483045,0.0000362634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004081721,0.0001034356,0.000611703,0.00001153918,0.00002114077,1.799891e-7,0.0002951916,0.00003366826,0.009436227,0.9157022,0.004314096,0.06942983],"study_design_scores_gemma":[0.00009823455,0.0003833613,0.00133297,0.00008637812,0.00001453206,0.0000122178,0.002099182,0.7037352,0.1382238,0.1137546,0.03991905,0.000340489],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2055355,0.0001083717,0.6518966,0.0955869,0.005580163,0.003977252,0.001768818,0.0008218652,0.03472452],"genre_scores_gemma":[0.9902862,0.00003255412,0.008732796,0.0003329972,0.0000582526,0.00006552386,0.00003667209,0.000007036788,0.0004479882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8019475,"threshold_uncertainty_score":0.5720181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1587867334931862,"score_gpt":0.3545600555866735,"score_spread":0.1957733220934873,"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."}}