{"id":"W4400446664","doi":"10.1109/tfuzz.2024.3422414","title":"Reinforced Fuzzy-Rule-Based Neural Networks Realized Through Streamlined Feature Selection Strategy and Fuzzy Clustering With Distance Variation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea; Ministry of Education, Libya","keywords":"Artificial intelligence; Fuzzy rule; Cluster analysis; Computer science; Feature selection; Fuzzy logic; Pattern recognition (psychology); Neuro-fuzzy; Selection (genetic algorithm); Artificial neural network; Data mining; Fuzzy set; Variation (astronomy); Fuzzy clustering; Feature (linguistics); Fuzzy control system; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007614245,0.0005986277,0.0007523441,0.0005492372,0.0003385602,0.0006700429,0.001175267,0.0008075128,0.0007237421],"category_scores_gemma":[0.002194316,0.0003490906,0.0007621203,0.0006048644,0.0004661768,0.0008954318,0.0005458349,0.0005717027,0.0002943759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000662246,"about_ca_system_score_gemma":0.0007161751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004380831,"about_ca_topic_score_gemma":0.004090693,"domain_scores_codex":[0.9993085,0.0001277049,0.00006349058,0.0001840977,0.0002660023,0.00005019262],"domain_scores_gemma":[0.9993538,0.0001898912,0.00009732959,0.00008490614,0.0002547462,0.00001928314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008810084,0.00005069309,0.0007047896,0.0001060608,0.00009641286,0.0001418663,0.0001258408,0.6856036,0.01741485,0.01489236,0.001080545,0.2796949],"study_design_scores_gemma":[0.00000379283,0.00002331868,0.0001386657,0.000005217201,0.000008426398,0.0000285516,0.000003162848,0.9948331,0.00234315,0.002020947,0.0005832859,0.00000847493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01230208,0.0001770789,0.9862584,0.0000441225,0.00002454153,0.00003511069,0.00001882156,0.0002570009,0.0008828182],"genre_scores_gemma":[0.432162,0.0002999359,0.5644553,0.0001015278,0.000043095,0.0002141176,0.0001407488,0.00005902242,0.002524201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004380831,"threshold_uncertainty_score":0.008710623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01202499432383265,"score_gpt":0.2241647035727065,"score_spread":0.2121397092488738,"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."}}