{"id":"W2607410741","doi":"10.1007/978-3-319-57351-9_11","title":"Classification of Imbalanced Auction Fraud Data","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Bidding; Class (philosophy); Binary classification; Sampling (signal processing); Set (abstract data type); Data mining; Domain (mathematical analysis); Artificial intelligence; Machine learning; Support vector machine","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":[0.007823735,0.001119278,0.001569978,0.005459469,0.0008513928,0.003301664,0.002097008,0.001698746,0.001475541],"category_scores_gemma":[0.02115366,0.0003854427,0.000874879,0.004395628,0.0008350334,0.003307961,0.001991726,0.002443924,0.001102122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228782,"about_ca_system_score_gemma":0.0009890684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001106876,"about_ca_topic_score_gemma":0.001153841,"domain_scores_codex":[0.9947173,0.001370994,0.0004838357,0.0006536046,0.002212723,0.0005616003],"domain_scores_gemma":[0.9888285,0.004867183,0.001297545,0.002536383,0.001916629,0.0005537419],"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":[0.001835632,0.001683409,0.1277654,0.0005123548,0.0004128881,0.0008159766,0.0005721261,0.08269315,0.003880817,0.01989029,0.06162523,0.6983128],"study_design_scores_gemma":[0.00007364385,0.0002036665,0.02100258,0.0001410048,0.00007821254,0.0008579614,0.0004995223,0.9103201,0.005982038,0.04574225,0.01505423,0.00004477512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6909562,0.006020907,0.2764316,0.005112827,0.001574809,0.0005554217,0.0084646,0.001822627,0.009061012],"genre_scores_gemma":[0.8930616,0.001199514,0.08462393,0.000360017,0.000814762,0.0001800362,0.01338096,0.0001344977,0.006244699],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007823735,"threshold_uncertainty_score":0.04137635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05525673183489122,"score_gpt":0.3080540741585807,"score_spread":0.2527973423236894,"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."}}