{"id":"W3039454837","doi":"10.1016/j.fss.2020.06.019","title":"Principles for constructing three-way approximations of fuzzy sets: A comparative evaluation based on unsupervised learning","year":2020,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Fuzzy logic; Mathematics; Approximations of π; Fuzzy set; Artificial intelligence; Set (abstract data type); Fuzzy number; Realization (probability); Machine learning; Computer science; Data mining; Statistics; Applied mathematics","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":[0.01824542,0.0008887711,0.001599542,0.006200226,0.001591683,0.005869385,0.003117682,0.001796726,0.002488912],"category_scores_gemma":[0.03129407,0.0006531713,0.002788833,0.003431027,0.005168204,0.007509658,0.003276388,0.002040653,0.0004900082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002553967,"about_ca_system_score_gemma":0.002194315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002739325,"about_ca_topic_score_gemma":0.002902534,"domain_scores_codex":[0.9881342,0.004858235,0.0009690361,0.0009077954,0.004859517,0.0002713416],"domain_scores_gemma":[0.9854468,0.006903088,0.0008792006,0.002197621,0.004281179,0.0002920247],"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":[0.0001365192,0.00008252907,0.002447482,0.0005535505,0.0002934898,0.00009812316,0.002040442,0.05500499,0.003623211,0.777887,0.001278205,0.1565545],"study_design_scores_gemma":[0.00003725771,0.0002416261,0.002498935,0.0003297206,0.0001619757,0.0003238613,0.001162759,0.3104308,0.006484588,0.6669912,0.01119628,0.0001410062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007065649,0.0003535153,0.9896545,0.0001521588,0.00001868263,0.00008090706,0.00003984108,0.00006561471,0.002569112],"genre_scores_gemma":[0.09709617,0.000348632,0.9016558,0.00003918263,0.00001911681,0.0001873357,0.00009089761,0.00005940978,0.0005034036],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01824542,"threshold_uncertainty_score":0.09649211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1530581882743704,"score_gpt":0.3093669688752433,"score_spread":0.1563087806008729,"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."}}