{"id":"W2143317258","doi":"10.1109/tsmcb.2005.852983","title":"Highly scalable and robust rule learner: performance evaluation and comparison","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Scalability; Data mining; Missing data; Set (abstract data type); Business intelligence; Simplicity; State (computer science); Knowledge extraction; Artificial intelligence; Machine learning; Algorithm; Database","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":[0.000527283,0.0002967552,0.0003399533,0.0001570614,0.0004587613,0.0006167522,0.000280165,0.0001452194,0.00001077375],"category_scores_gemma":[0.000003096529,0.0002952386,0.00003672653,0.00028261,0.000194484,0.0003103293,0.00001739107,0.0002613071,0.00004788585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004911124,"about_ca_system_score_gemma":0.00004164534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005234644,"about_ca_topic_score_gemma":0.0001084035,"domain_scores_codex":[0.9977947,0.0001142002,0.0005151285,0.00070981,0.0004913775,0.0003747813],"domain_scores_gemma":[0.9988234,0.0001062163,0.0001673257,0.000568145,0.0001469108,0.0001880167],"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":[0.00005886028,0.001536389,0.005969079,0.000496038,0.0002333343,0.00001052356,0.002449868,0.1868875,0.001567635,0.03067632,0.01363719,0.7564773],"study_design_scores_gemma":[0.0009118065,0.0002375528,0.006838828,0.0001434465,0.0000992391,0.00006525498,0.0001341946,0.9713165,0.001367517,0.0001933654,0.01823905,0.0004532937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5962031,0.002196789,0.3946213,0.0004706296,0.0007462732,0.0009140683,0.00006670797,0.0002489201,0.004532278],"genre_scores_gemma":[0.9891848,0.0006029189,0.006304931,0.00004439835,0.000109639,0.0001435838,0.0000174153,0.00002775139,0.003564587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.784429,"threshold_uncertainty_score":0.99995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0260826067644924,"score_gpt":0.2476861809748204,"score_spread":0.221603574210328,"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."}}