{"id":"W4399801303","doi":"10.1109/tnnls.2024.3408208","title":"Bi-Level Spectral Feature Selection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Feature selection; Computer science; Cluster analysis; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Linear classifier; Data mining; Feature (linguistics); Machine learning","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":[0.0001855074,0.0001607278,0.0001409387,0.0001519743,0.0004314149,0.0006233153,0.0001186877,0.0001351262,0.00001251394],"category_scores_gemma":[0.000001826582,0.0001318736,0.00008009522,0.0004513887,0.00001936748,0.000423241,0.000001662343,0.0009082194,0.00002787579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002744291,"about_ca_system_score_gemma":0.00001354922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000556497,"about_ca_topic_score_gemma":0.00001025539,"domain_scores_codex":[0.9988593,0.0001749902,0.0001483749,0.0003908926,0.0001702434,0.0002562081],"domain_scores_gemma":[0.9996002,0.000134209,0.00003353338,0.0001093174,0.00003075538,0.00009194643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001301421,0.0000192189,0.0000188494,0.00004904365,0.00002943678,0.00001830747,0.0001959775,0.8923542,0.0007345811,0.0001961204,0.002149455,0.1042218],"study_design_scores_gemma":[0.0001130332,0.0001672529,0.0001051641,0.0001993707,0.00001305819,0.0001763588,0.00004934439,0.993281,0.0002366204,0.00001126879,0.005486941,0.0001605876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01983132,0.0008810353,0.9746931,0.0005526204,0.003154151,0.0001512155,0.000002173383,0.0005793254,0.0001551022],"genre_scores_gemma":[0.9948068,0.0001443386,0.0003903355,0.00007264316,0.0002903396,0.00002788842,0.000002159877,0.00001665971,0.004248835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9749755,"threshold_uncertainty_score":0.6010644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781432681971721,"score_gpt":0.2351512350278215,"score_spread":0.2173369082081043,"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."}}