{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000511846,0.0002785503,0.0003144843,0.0002753537,0.0002996686,0.0004155443,0.0006620663,0.00061492,0.000009399067],"category_scores_gemma":[0.0006223858,0.00028142,0.0001039537,0.0002224676,0.000007549912,0.001466282,0.00005670903,0.0003533749,0.000006928492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006381366,"about_ca_system_score_gemma":0.0001191592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003451282,"about_ca_topic_score_gemma":0.01005138,"domain_scores_codex":[0.9980252,0.0000520843,0.0005160528,0.0009078883,0.0003024785,0.0001962433],"domain_scores_gemma":[0.9976674,0.0006658796,0.0005998882,0.0005054637,0.0004685158,0.00009280442],"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":[0.00005909003,0.00003167746,0.000003948254,0.00003746758,0.00001327748,3.57946e-8,0.0001238664,0.000006546908,0.1428749,0.01649328,0.0001988333,0.8401571],"study_design_scores_gemma":[0.0006894574,0.0002245528,0.005898902,0.00008226203,0.00007972395,0.000001989399,0.0001150182,0.5923009,0.250395,0.1318509,0.01785287,0.0005084223],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007455103,0.0001348655,0.9893951,0.0001134163,0.0004501136,0.001714832,0.0001514817,0.000543949,0.00004113324],"genre_scores_gemma":[0.01145804,0.0001113949,0.9781396,0.00004081698,0.0003227762,0.0006305514,0.007799111,0.00003701204,0.001460723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8396487,"threshold_uncertainty_score":0.9999638,"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."}}