{"id":"W2124227618","doi":"10.1109/tsmcb.2002.1018771","title":"COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Fuzzy rule; Set (abstract data type); Fuzzy logic; Simple (philosophy); Subspace topology; Artificial intelligence; Rule-based system; Data mining; Post hoc; Machine learning; Fuzzy set; Programming language","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":[0.00162132,0.0005560894,0.0008588536,0.0002784638,0.0005351073,0.0007090135,0.001212831,0.000376387,0.00003000731],"category_scores_gemma":[0.0001519286,0.0005404079,0.0001170985,0.0004314128,0.0001713094,0.0002949954,0.00005907076,0.0007421898,0.0002316347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001167286,"about_ca_system_score_gemma":0.00004482068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004882323,"about_ca_topic_score_gemma":0.0001453997,"domain_scores_codex":[0.9948186,0.001402434,0.001021821,0.00147718,0.0005151255,0.0007648054],"domain_scores_gemma":[0.9964933,0.0009168223,0.0003567679,0.001482098,0.0002551322,0.0004958395],"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":[0.0002184609,0.001545176,0.0004072883,0.0007300304,0.001489247,0.0001669753,0.02714609,0.1298594,0.04567516,0.01967342,0.01555114,0.7575375],"study_design_scores_gemma":[0.001958662,0.001499415,0.0001612653,0.0003736793,0.0002826736,0.0001312883,0.0006282551,0.9163491,0.002697997,0.0004046219,0.07402465,0.001488366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02489873,0.002823425,0.9640678,0.0002444467,0.003128834,0.001052821,0.0000983001,0.0003622903,0.003323428],"genre_scores_gemma":[0.9544733,0.0006680606,0.03519877,0.0002412727,0.0002901058,0.0002372656,0.00002479484,0.00007258355,0.008793814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9295746,"threshold_uncertainty_score":0.9997047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06672284856173605,"score_gpt":0.300670168457369,"score_spread":0.233947319895633,"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."}}