{"id":"W2133960219","doi":"10.1109/fuzz.2003.1209448","title":"Feature region-merging based fuzzy rules extraction for pattern classification","year":2004,"lang":"en","type":"article","venue":"","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Computer science; Artificial intelligence; Fuzzy logic; Feature extraction; Fuzzy set; Class (philosophy); Feature vector; Data mining; Representation (politics); Feature (linguistics); Process (computing); Set (abstract data type)","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":[0.0001523078,0.0001019303,0.0001058912,0.00006320982,0.0001441073,0.0001331766,0.0003186745,0.0000854582,9.701243e-7],"category_scores_gemma":[0.00002051523,0.00008200144,0.00008117832,0.0001160743,0.00001251913,0.0003661902,0.00001640652,0.00007089568,0.0000366982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008128978,"about_ca_system_score_gemma":0.00005937031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005269572,"about_ca_topic_score_gemma":0.00002050075,"domain_scores_codex":[0.9991922,0.00002599249,0.0001223073,0.0003110344,0.0001604822,0.0001880307],"domain_scores_gemma":[0.9993421,0.00007390027,0.00009731654,0.0003529107,0.00008040288,0.00005334184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002788934,0.0001496868,0.0008743342,0.00006886802,0.00002544748,0.00001538443,0.0002433169,0.001657633,0.006858164,0.6492649,0.009379609,0.3314347],"study_design_scores_gemma":[0.01055972,0.0007294461,0.06769147,0.0003535068,0.00006491719,0.0001936307,0.0008356216,0.5487634,0.004704736,0.2688908,0.09531406,0.001898696],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001454137,0.00007298835,0.9668386,0.02107895,0.0004196173,0.0002866062,0.000001088626,0.0002418911,0.009606138],"genre_scores_gemma":[0.9738004,0.000003156889,0.02437717,0.0008615719,0.0001835851,0.00008932063,0.00000877109,0.000006740172,0.000669259],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9723463,"threshold_uncertainty_score":0.3343922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02820565395586552,"score_gpt":0.2549896857647419,"score_spread":0.2267840318088764,"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."}}