{"id":"W4394981804","doi":"10.1016/j.ins.2024.120647","title":"Multi-association evidential feature selection and its application to identifying schizophrenia","year":2024,"lang":"en","type":"article","venue":"Information Sciences","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Feature selection; Selection (genetic algorithm); Schizophrenia (object-oriented programming); Association (psychology); Computer science; Feature (linguistics); Artificial intelligence; Evidential reasoning approach; Pattern recognition (psychology); Data mining; Machine learning; Psychology; Decision support system; Psychotherapist","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008260848,0.00007663341,0.00006415667,0.0003035918,0.0003650911,0.002029216,0.0003286256,0.00005886809,0.000003448269],"category_scores_gemma":[0.000102002,0.00006352479,0.00002378953,0.001320223,0.00001065234,0.006351027,0.0001086679,0.000090468,0.0003284519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000847751,"about_ca_system_score_gemma":0.00006545361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002674355,"about_ca_topic_score_gemma":0.00003125656,"domain_scores_codex":[0.9989831,0.00002444859,0.0001865684,0.0002030552,0.0004350405,0.0001677391],"domain_scores_gemma":[0.9996252,0.00004922457,0.00008373463,0.00007576255,0.0001063035,0.00005976937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001121652,0.00002731292,0.001715782,0.000180808,0.00003069202,0.00000115832,0.01166118,0.005331298,0.01080505,0.09212623,0.00894632,0.869163],"study_design_scores_gemma":[0.0001040132,0.00004031543,0.01350096,0.00003520261,0.000004423517,0.000011438,0.00007588365,0.9671896,0.0007523698,0.0008044288,0.01734805,0.000133343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04367738,0.0003691874,0.9492063,0.004430759,0.0009041677,0.0003468563,0.000003540887,0.0003598492,0.0007019415],"genre_scores_gemma":[0.9638365,0.00003673384,0.03556283,0.0003702385,0.00008029265,0.00003142671,0.000003138007,0.000001675315,0.00007716737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9618583,"threshold_uncertainty_score":0.9990067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02385081771858439,"score_gpt":0.3015634790128809,"score_spread":0.2777126612942965,"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."}}