{"id":"W2590043730","doi":"10.1016/s1005-8885(16)60065-1","title":"Attribute reduction based on fuzziness of approximation set in multi-granulation spaces","year":2016,"lang":"en","type":"article","venue":"The Journal of China Universities of Posts and Telecommunications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ryerson University","keywords":"Rough set; Reduction (mathematics); Granular computing; Granularity; Set (abstract data type); Heuristic; Boundary (topology); Mathematics; Space (punctuation); Computer science; Algorithm; Data mining; Mathematical optimization","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049561,0.0000555022,0.0001335166,0.0002140328,0.00009586122,0.00001230755,0.000508528,0.0000269499,0.00000276302],"category_scores_gemma":[0.00002098688,0.00003230314,0.00003636935,0.0002067725,0.0001126813,0.0004140838,0.00007123728,0.00008211995,1.772285e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004221413,"about_ca_system_score_gemma":0.00005799322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008030882,"about_ca_topic_score_gemma":0.00001448519,"domain_scores_codex":[0.9993584,0.0001866812,0.0002259326,0.00004363895,0.0001206345,0.00006469055],"domain_scores_gemma":[0.9988993,0.0002031386,0.0004122143,0.0003244588,0.0001413304,0.00001951811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00281859,0.00489957,0.04295833,0.0006789843,0.0005960187,0.00001481707,0.1310914,0.1700698,0.06195361,0.3584518,0.001921246,0.2245458],"study_design_scores_gemma":[0.006238916,0.001563852,0.8554834,0.0009479832,0.0001302775,0.00008244775,0.009571149,0.1118991,0.006878556,0.006232071,0.0005979592,0.0003742972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8782672,0.0001674492,0.1114123,0.009550968,0.00005085782,0.00009245567,0.00001125714,0.000006335096,0.0004411361],"genre_scores_gemma":[0.9866369,0.0003828703,0.01294282,0.00001066976,0.00000717996,1.856597e-7,0.000001967025,0.000001997091,0.00001537818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8125251,"threshold_uncertainty_score":0.1317284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02429517762754166,"score_gpt":0.2532156700806225,"score_spread":0.2289204924530808,"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."}}