{"id":"W2167102385","doi":"10.1109/icmla.2009.25","title":"A New Method for Learning Decision Trees from Rules","year":2009,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Incremental decision tree; ID3 algorithm; Decision tree; Computer science; Decision tree learning; Machine learning; Data mining; Decision stump; Artificial intelligence; Decision rule; Tree (set theory); Influence diagram; Decision engineering; Set (abstract data type); Alternating decision tree; Business decision mapping; Decision support system; Mathematics","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":[0.0002921331,0.00007479687,0.00009626333,0.00005820224,0.0001098368,0.0001883633,0.0004564379,0.00003994352,0.00004725788],"category_scores_gemma":[0.000294064,0.00005921298,0.00004571941,0.0001113634,0.000002431615,0.0002783528,0.00004427076,0.00009131211,0.00007830882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000010269,"about_ca_system_score_gemma":0.00003040566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002512437,"about_ca_topic_score_gemma":0.00002783094,"domain_scores_codex":[0.9992447,0.00005745643,0.0001325594,0.0003092051,0.0001312199,0.0001248469],"domain_scores_gemma":[0.9989879,0.0005246549,0.0000564342,0.0003321571,0.00002682769,0.00007199025],"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":[0.000007049625,0.000008564395,0.0001447507,2.994059e-7,0.000001990338,2.207217e-7,0.0001073375,0.0002471142,0.001076768,0.06010562,0.004586784,0.9337135],"study_design_scores_gemma":[0.0003775614,0.0001380942,0.03719288,0.00001118953,0.000004804878,0.000001494071,0.00001587424,0.7082962,0.0006408183,0.09468732,0.1585082,0.0001255278],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000761766,0.00007471827,0.9925386,0.003223508,0.00007423206,0.00006730574,0.000001345833,0.0002697843,0.00298876],"genre_scores_gemma":[0.02494119,0.000007216467,0.9723988,0.0003476389,0.00009920949,0.000002311372,0.000033327,0.000003285003,0.002166984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.933588,"threshold_uncertainty_score":0.2414635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232669115739505,"score_gpt":0.3396877144381064,"score_spread":0.3164208028641559,"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."}}