{"id":"W2223602837","doi":"","title":"A new method for learning decision trees from rules and its illustration for online identity application fraud detection","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Decision tree; ID3 algorithm; Incremental decision tree; Decision tree learning; Computer science; Machine learning; Decision rule; Data mining; Artificial intelligence; Alternating decision tree; Decision stump; Tree (set theory); Set (abstract data type); Influence diagram; Decision engineering; Decision analysis; Mathematics; Business decision mapping; Decision support system; Statistics","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":[0.002536644,0.001082395,0.0007694275,0.003062977,0.0007225774,0.001637228,0.001698394,0.001359517,0.003901446],"category_scores_gemma":[0.009739175,0.0005895054,0.002077524,0.002636196,0.0007301588,0.002463831,0.001247689,0.003011275,0.001314773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047713,"about_ca_system_score_gemma":0.001789046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003315475,"about_ca_topic_score_gemma":0.003958491,"domain_scores_codex":[0.9976357,0.000634601,0.0002364174,0.0004751084,0.0009286999,0.00008943045],"domain_scores_gemma":[0.9956549,0.002743444,0.0002653899,0.0004293382,0.000767038,0.0001398766],"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":[0.000177184,0.0002775614,0.003263596,0.0004590634,0.0001660592,0.0002975017,0.0002730641,0.110741,0.005513959,0.06289121,0.01791956,0.7980203],"study_design_scores_gemma":[0.00004677007,0.00007601188,0.0006672331,0.00007953368,0.00004410871,0.0003843895,0.00003623889,0.9225878,0.004330238,0.05147442,0.02022967,0.00004366673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002106256,0.000260807,0.9946941,0.0002247494,0.00007648199,0.0001087146,0.0002700735,0.001110231,0.001148545],"genre_scores_gemma":[0.05000721,0.0003849491,0.9464593,0.0002064697,0.00007486222,0.0002668123,0.0008042033,0.0001225833,0.00167358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003901446,"threshold_uncertainty_score":0.01341522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02604447998424599,"score_gpt":0.3619096922652578,"score_spread":0.3358652122810118,"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."}}