{"id":"W2087805359","doi":"10.1109/eisic.2012.51","title":"Data Mining Applications for Fraud Detection in Securities Market","year":2012,"lang":"en","type":"article","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates","keywords":"Computer science; Security market; Data science; Securities fraud; Business; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005545815,0.001189861,0.001463961,0.009733562,0.001119204,0.003224337,0.001387307,0.001529556,0.001983776],"category_scores_gemma":[0.02300914,0.0005304681,0.001046086,0.009536964,0.0005530271,0.00310045,0.0014852,0.002082495,0.001355799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009115409,"about_ca_system_score_gemma":0.0009811106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103226,"about_ca_topic_score_gemma":0.001179986,"domain_scores_codex":[0.9963021,0.001032989,0.0006056339,0.0004189573,0.001528081,0.0001122874],"domain_scores_gemma":[0.9852573,0.008450896,0.001917328,0.001403196,0.002698892,0.0002723754],"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":[0.0004486244,0.0007099294,0.05616131,0.001573659,0.0003601582,0.001128262,0.0006558036,0.03370363,0.00889782,0.02269457,0.02269223,0.8509739],"study_design_scores_gemma":[0.000122793,0.0003201059,0.02299096,0.0009947281,0.0003003883,0.002579715,0.000813145,0.7516258,0.02938345,0.09724303,0.09348167,0.0001442217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09957868,0.01736092,0.8495112,0.00830127,0.0007605257,0.001153691,0.004663314,0.007403478,0.01126699],"genre_scores_gemma":[0.3903531,0.007943168,0.5946006,0.0008227371,0.0005821494,0.0005614308,0.002976112,0.0001690112,0.001991789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009733562,"threshold_uncertainty_score":0.02932942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06259008482857968,"score_gpt":0.318858635269936,"score_spread":0.2562685504413563,"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."}}