{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003473251,0.0003597845,0.0009964544,0.001816932,0.0004613234,0.0009437874,0.0005373409,0.0005786292,0.000641306],"category_scores_gemma":[0.006690683,0.0001855085,0.0009206013,0.001543621,0.0003277283,0.0006414391,0.0005627997,0.000577916,0.0001286608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002275166,"about_ca_system_score_gemma":0.0006339309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001569639,"about_ca_topic_score_gemma":0.001540252,"domain_scores_codex":[0.9994802,0.0002687432,0.00005408187,0.0000703214,0.0000991596,0.00002750796],"domain_scores_gemma":[0.9968388,0.002495375,0.0001592199,0.0001524876,0.000310704,0.00004339447],"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":[0.0009001465,0.0004128246,0.02333408,0.0004132783,0.0005695221,0.001071491,0.0004622406,0.2060676,0.01812704,0.02398816,0.002100112,0.7225536],"study_design_scores_gemma":[0.00002435203,0.0001417707,0.01209298,0.00002294553,0.0001459426,0.0003587782,0.0000729789,0.9655199,0.002398046,0.01840231,0.0007800661,0.00003990936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1311484,0.001715187,0.8652371,0.0004527762,0.00006520882,0.00005511983,0.0001747407,0.000188052,0.0009634275],"genre_scores_gemma":[0.8059202,0.0006630108,0.1925185,0.0000414482,0.00005849103,0.00004371999,0.0001321141,0.00001439398,0.0006080596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003473251,"threshold_uncertainty_score":0.01836854,"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."}}