{"id":"W3006217971","doi":"10.1109/tfuzz.2020.2973956","title":"Granular Aggregation of Fuzzy Rule-Based Models in Distributed Data Environment","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canada First Research Excellence Fund; Natural Science Foundation of Shaanxi Province; Science Foundation for Excellent Youth Scholars of Sichuan University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Center for Selective C-H Functionalization, National Science Foundation","keywords":"Granularity; Granular computing; Aggregate (composite); Computer science; Data mining; Fuzzy logic; Process (computing); Fuzzy set; Interval (graph theory); Fuzzy control system; Artificial intelligence; Mathematics; Rough set","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005122847,0.0006847787,0.00153899,0.001779013,0.0006795822,0.004408755,0.001157373,0.0009014987,0.001093213],"category_scores_gemma":[0.0097909,0.0005260727,0.001491272,0.002172946,0.001346094,0.00258135,0.001779976,0.001071893,0.0001909983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001941311,"about_ca_system_score_gemma":0.001134796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006461729,"about_ca_topic_score_gemma":0.005952691,"domain_scores_codex":[0.9961751,0.001260716,0.0004260591,0.0005009524,0.001382489,0.0002546258],"domain_scores_gemma":[0.9952683,0.002700162,0.0004655939,0.0008715154,0.0005316513,0.0001627731],"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":[0.0001184762,0.00004959246,0.001158577,0.0001154266,0.00008582155,0.0002383866,0.000368403,0.8877988,0.002121152,0.07427353,0.0003792296,0.03329251],"study_design_scores_gemma":[0.00000940039,0.0000332235,0.0002159757,0.00002222821,0.00003124633,0.00003168553,0.00006182422,0.9525984,0.0008250827,0.04553997,0.0006174552,0.00001354359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03921784,0.000282201,0.9577649,0.0002002178,0.00002149941,0.00007939255,0.0001235668,0.0002647893,0.002045554],"genre_scores_gemma":[0.6595575,0.0003475724,0.3386275,0.00005645562,0.00003118834,0.0001341001,0.0002468881,0.00004579648,0.0009529558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006461729,"threshold_uncertainty_score":0.02709252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04416402866269045,"score_gpt":0.2225018017181567,"score_spread":0.1783377730554662,"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."}}