{"id":"W1505953715","doi":"10.1007/978-3-540-74484-9_94","title":"Identification of Fuzzy Set-Based Fuzzy Systems by Means of Data Granulation and Genetic Optimization","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy set operations; Fuzzy classification; Fuzzy set; Defuzzification; Fuzzy number; Fuzzy logic; Mathematics; Fuzzy clustering; Genetic algorithm; Neuro-fuzzy; Data mining; Mathematical optimization; Membership function; Algorithm; Computer science; Fuzzy control system; Artificial intelligence","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":[0.001526293,0.0003161441,0.0005756737,0.0006031221,0.0001057295,0.0002251705,0.002574391,0.0002810186,8.756241e-7],"category_scores_gemma":[0.00006928106,0.0002932536,0.00005100275,0.0005311354,0.0004640038,0.0005801854,0.0005171165,0.0002163945,0.000001616121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009305957,"about_ca_system_score_gemma":0.0002411348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009809186,"about_ca_topic_score_gemma":0.00002578146,"domain_scores_codex":[0.996404,0.00007493284,0.001051191,0.001155104,0.001003365,0.0003113909],"domain_scores_gemma":[0.9963531,0.0003654126,0.0009627221,0.001836765,0.0003911199,0.00009089374],"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":[0.0000126612,0.00003018053,0.0001747664,0.0002751867,0.00001640033,0.000006706053,0.0002360638,0.8593094,0.001482722,0.009652143,0.00002609294,0.1287776],"study_design_scores_gemma":[0.0003437534,0.00009267349,0.0001735179,0.0002677043,0.00001855951,0.00001507159,3.959146e-7,0.9892104,0.0003726228,0.009188301,0.00004636225,0.0002706957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000105507,0.001968659,0.9955422,0.0001135101,0.0007586709,0.0005742477,0.00005734701,0.00005002691,0.000829787],"genre_scores_gemma":[0.8246509,0.00006399168,0.1748705,0.0001003065,0.0001663389,0.000006112987,0.00007581434,0.00002119127,0.00004482445],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8245454,"threshold_uncertainty_score":0.999952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02919831820857533,"score_gpt":0.2536502195566986,"score_spread":0.2244519013481233,"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."}}