{"id":"W4394862804","doi":"10.1109/tkde.2024.3388526","title":"Feature Selection With Discernibility and Independence Criteria","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Feature selection; Selection (genetic algorithm); Independence (probability theory); Artificial intelligence; Feature (linguistics); Data mining; Pattern recognition (psychology); Mathematics; Statistics","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":[0.000116475,0.0001093553,0.00008085932,0.0001063473,0.00009435583,0.0002336154,0.0001638849,0.000061206,0.000008818361],"category_scores_gemma":[0.000002211905,0.00008615091,0.0000105023,0.0002333712,0.00001494776,0.001019191,0.000009088306,0.0002397547,0.00001258638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001386713,"about_ca_system_score_gemma":0.00002475034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007203945,"about_ca_topic_score_gemma":0.00003461459,"domain_scores_codex":[0.9993067,0.00001314134,0.0000601854,0.0004214883,0.00008190286,0.0001165255],"domain_scores_gemma":[0.9995645,0.0000577741,0.00000646015,0.0002809505,0.00002268609,0.00006766125],"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":[0.00007802284,0.0002726352,0.00009108189,0.001449054,0.0001829717,0.00005323061,0.00272788,0.003511889,0.05168477,0.0008316513,0.007024709,0.9320921],"study_design_scores_gemma":[0.0001708634,0.0001005148,0.0003523731,0.0003545721,0.00002526637,0.0001189699,0.00001974476,0.9744197,0.01568709,0.00004388062,0.008492736,0.0002143592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01215237,0.0006183096,0.9862115,0.0002232416,0.0003528615,0.00007737901,0.00004455124,0.0002536295,0.00006619687],"genre_scores_gemma":[0.99054,0.0001649913,0.009024257,0.00001209993,0.00004245159,0.0000131015,0.000008458558,0.000009424627,0.0001852368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9783876,"threshold_uncertainty_score":0.3513132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885862692112788,"score_gpt":0.2740728741851766,"score_spread":0.2552142472640487,"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."}}