{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002145638,0.0009860229,0.001583319,0.002697309,0.0006596495,0.0009938452,0.001170646,0.0007648195,0.0008413338],"category_scores_gemma":[0.005078639,0.0003592853,0.001068448,0.001534285,0.0004838675,0.0008806625,0.0006561665,0.0009271911,0.0003540738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004766242,"about_ca_system_score_gemma":0.0007723729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002101544,"about_ca_topic_score_gemma":0.001748398,"domain_scores_codex":[0.998131,0.0004269286,0.0001696567,0.0003243785,0.000849569,0.00009847915],"domain_scores_gemma":[0.998354,0.0008636494,0.00011281,0.0001301394,0.0005048949,0.00003465163],"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":[0.0001415469,0.0001051635,0.001579414,0.0001052157,0.0001034845,0.0001386875,0.0001179778,0.06445102,0.01745282,0.004098579,0.001337865,0.9103682],"study_design_scores_gemma":[0.0000342661,0.0001071371,0.001050496,0.00001811426,0.00003275946,0.0001981239,0.00003445984,0.9779862,0.01352925,0.004777839,0.002196329,0.00003508997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007330124,0.0001388884,0.9919986,0.0000355238,0.0000187917,0.00004561928,0.0000238051,0.000242446,0.0001661839],"genre_scores_gemma":[0.1146015,0.0001426555,0.8843041,0.00004507976,0.00003822157,0.0001939904,0.0001731988,0.0000373627,0.0004638762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002697309,"threshold_uncertainty_score":0.01134735,"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."}}