{"id":"W2056784354","doi":"10.1016/j.ijar.2010.01.004","title":"Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications","year":2010,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":227,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Rough set; Kernel embedding of distributions; Kernel method; Fuzzy number; Artificial intelligence; Fuzzy set; Pattern recognition (psychology); Fuzzy logic; Data mining; Computer science; Support vector machine","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":[0.003032247,0.0005951534,0.001924274,0.001892305,0.0005739576,0.003186788,0.001737702,0.001434663,0.0007354498],"category_scores_gemma":[0.01123402,0.0004625804,0.001442788,0.002613455,0.001369586,0.003862978,0.001050891,0.001426585,0.0002336693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001581334,"about_ca_system_score_gemma":0.0009881636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003525195,"about_ca_topic_score_gemma":0.001740472,"domain_scores_codex":[0.9978659,0.0007829813,0.0001377078,0.0002093537,0.0008472019,0.0001568322],"domain_scores_gemma":[0.9959952,0.002029855,0.000515716,0.0004153166,0.0009170492,0.0001267523],"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":[0.0002390517,0.0001033842,0.002032507,0.0003093737,0.0002297114,0.000267647,0.0003983047,0.4131892,0.002447995,0.4907829,0.002121869,0.08787798],"study_design_scores_gemma":[0.000008543609,0.00002991315,0.0004389614,0.00001557101,0.00003601813,0.00007192014,0.00004214272,0.8773086,0.0005297749,0.1207596,0.0007254428,0.00003361414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02846746,0.001444102,0.9684573,0.0003029208,0.0000565545,0.00002232985,0.00007498344,0.0001046373,0.001069732],"genre_scores_gemma":[0.8297104,0.001937301,0.1654948,0.00007613377,0.0001276686,0.00008344651,0.0001796514,0.00004318122,0.002347281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003525195,"threshold_uncertainty_score":0.01603627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587560862535223,"score_gpt":0.2704050235837117,"score_spread":0.2545294149583594,"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."}}