{"id":"W2129943987","doi":"10.1109/iccet.2009.36","title":"Features Selection Using Fuzzy ESVDF for Data Dimensionality Reduction","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Feature selection; Computer science; Dimensionality reduction; Fuzzy logic; Artificial intelligence; Curse of dimensionality; Data mining; Fuzzy set; Selection (genetic algorithm); Reduction (mathematics); Weight; Fuzzy classification; Pattern recognition (psychology); Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002148796,0.00006996807,0.00006927997,0.000051455,0.0002135944,0.00009365814,0.0002974828,0.00005404994,0.0000124354],"category_scores_gemma":[0.00003017923,0.00005812407,0.00002591278,0.0001801,0.000007906639,0.0009800197,0.00007608867,0.00005731129,0.000008606716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002251394,"about_ca_system_score_gemma":0.00003391259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004610007,"about_ca_topic_score_gemma":0.000004092278,"domain_scores_codex":[0.9992368,0.00002800265,0.000111386,0.0003466324,0.0001398874,0.0001372551],"domain_scores_gemma":[0.9994404,0.00002132765,0.00004635511,0.000361191,0.00008994778,0.00004081814],"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":[0.00005950128,0.0002076265,0.00003065159,0.00001436977,0.00001624313,8.097697e-7,0.0001070737,0.0004526421,0.3919989,0.02789739,0.1844152,0.3947996],"study_design_scores_gemma":[0.0009764695,0.0003055917,0.005506286,0.00008464363,0.00003545692,0.000205164,0.00008573906,0.6180603,0.225349,0.1349105,0.01390469,0.0005762255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03795001,0.00006634412,0.9557472,0.003173436,0.0006196075,0.0002705553,0.000006322497,0.0002637535,0.001902765],"genre_scores_gemma":[0.5196506,0.000009615217,0.4788214,0.0005738932,0.0002245458,0.000004130739,0.00009362053,0.000004154203,0.0006180103],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6176077,"threshold_uncertainty_score":0.2370231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08150717625596637,"score_gpt":0.3389680996098124,"score_spread":0.2574609233538461,"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."}}